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Found 380 papers, 158 papers with code
H2KGAT: Hierarchical Hyperbolic Knowledge Graph Attention Network
Existing knowledge graph embedding approaches concentrate on modeling symmetry/asymmetry, inversion, and composition typed relations but overlook the hierarchical nature of relations.
PDALN: Progressive Domain Adaptation over a Pre-trained Model for Low-Resource Cross-Domain Named Entity Recognition
Cross-domain Named Entity Recognition (NER) transfers the NER knowledge from high-resource domains to the low-resource target domain.
Relational Prompt-based Pre-trained Language Models for Social Event Detection
In this paper, we approach social event detection from a new perspective based on Pre-trained Language Models (PLMs), and present RPLM_SED (Relational prompt-based Pre-trained Language Models for Social Event Detection).
Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers
To this end, in this paper, we present a thorough review and provide a unified perspective to summarize the recent progress as well as emerging trends in multilingual large language models (MLLMs) literature.
Instruction-based Hypergraph Pretraining
However, the gap between training objectives and the discrepancy between data distributions in pretraining and downstream tasks hinders the transfer of the pretrained knowledge.
Large Language Models for Education: A Survey and Outlook
The advent of Large Language Models (LLMs) has brought in a new era of possibilities in the realm of education.
Uncertainty in Graph Neural Networks: A Survey
Therefore, identifying, quantifying, and utilizing uncertainty are essential to enhance the performance of the model for the downstream tasks as well as the reliability of the GNN predictions.
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges
To tackle these issues, substantial efforts have been devoted to improving the performance of GNN models in practical real-world scenarios, as well as enhancing their reliability and robustness.
Against Filter Bubbles: Diversified Music Recommendation via Weighted Hypergraph Embedding Learning
In the DWHRec algorithm, the initial connections between users and listened tracks are represented by a weighted hypergraph.
Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions
To this end, we propose evaluating the robustness of generative search engines in the realistic and high-risk setting, where adversaries have only black-box system access and seek to deceive the model into returning incorrect responses.
Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks
The rise of self-supervised learning, which operates without the need for labeled data, has garnered significant interest within the graph learning community.
Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction
To promote the CGEC field to better adapt to the era of LLMs, we rethink the roles of LLMs in the CGEC task so that they can be better utilized and explored in CGEC.
Disclosure and Mitigation of Gender Bias in LLMs
Our experiments demonstrate that all tested LLMs exhibit explicit and/or implicit gender bias, even when gender stereotypes are not present in the inputs.
When LLMs Meet Cunning Questions: A Fallacy Understanding Benchmark for Large Language Models
In this paper, we challenge the reasoning and understanding abilities of LLMs by proposing a FaLlacy Understanding Benchmark (FLUB) containing cunning questions that are easy for humans to understand but difficult for models to grasp.
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction
Recognizing the critical role of confidence in aligning training objectives with evaluation metrics, we propose CPFT, a versatile framework that enhances recommendation confidence by integrating Conformal Prediction (CP)-based losses with CE loss during fine-tuning.
Rec-GPT4V: Multimodal Recommendation with Large Vision-Language Models
We utilize user history as in-context user preferences to address the first challenge.
Multitask Active Learning for Graph Anomaly Detection
Firstly, by coupling node classification tasks, MITIGATE obtains the capability to detect out-of-distribution nodes without known anomalies.
DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing
Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined graph.
Cyclic Neural Network
This paper answers a fundamental question in artificial neural network (ANN) design: We do not need to build ANNs layer-by-layer sequentially to guarantee the Directed Acyclic Graph (DAG) property.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
Contrastive Sequential Interaction Network Learning on Co-Evolving Riemannian Spaces
To address the aforementioned issues, we propose a novel Contrastive model for Sequential Interaction Network learning on Co-Evolving RiEmannian spaces, CSINCERE.
Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit
We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-source toolkit tailored for the rapid prototyping of deep-learning-based code intelligence models.
Data Augmentation for Supervised Graph Outlier Detection with Latent Diffusion Models
One of the fundamental challenges confronting supervised graph outlier detection algorithms is the prevalent issue of class imbalance, where the scarcity of outlier instances compared to normal instances often results in suboptimal performance.
Hypergraph Enhanced Knowledge Tree Prompt Learning for Next-Basket Recommendation
To settle the above challenges, we propose a novel method HEKP4NBR, which transforms the knowledge graph (KG) into prompts, namely Knowledge Tree Prompt (KTP), to help PLM encode the OOV item IDs in the user's basket sequence.
Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection
As a trending approach for social event detection, graph neural network (GNN)-based methods enable a fusion of natural language semantics and the complex social network structural information, thus showing SOTA performance.
DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation
With our principle, we managed to outperform GPT-Turbo-3. 5 on three datasets using 7b models e. g., Vicuna-7b and Openchat-7b on NDCG@10.
ShuttleSHAP: A Turn-Based Feature Attribution Approach for Analyzing Forecasting Models in Badminton
Agent forecasting systems have been explored to investigate agent patterns and improve decision-making in various domains, e. g., pedestrian predictions and marketing bidding.
Prompt Based Tri-Channel Graph Convolution Neural Network for Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) is an emerging task to extract a given sentence's triplets, which consist of aspects, opinions, and sentiments.
kNN-ICL: Compositional Task-Oriented Parsing Generalization with Nearest Neighbor In-Context Learning
Task-Oriented Parsing (TOP) enables conversational assistants to interpret user commands expressed in natural language, transforming them into structured outputs that combine elements of both natural language and intent/slot tags.
A Survey of Text Watermarking in the Era of Large Language Models
Text watermarking algorithms play a crucial role in the copyright protection of textual content, yet their capabilities and application scenarios have been limited historically.
Large Language Models in Law: A Survey
With the rapid emergence and growing popularity of large models, it is evident that AI will drive transformation in the traditional judicial industry.
Multimodal Large Language Models: A Survey
By addressing these aspects, this paper aims to facilitate a deeper understanding of multimodal models and their potential in various domains.
Graph Neural Ordinary Differential Equations-based method for Collaborative Filtering
This inspired us to address the computational limitations of GCN-based models by designing a simple and efficient NODE-based model that can skip some GCN layers to reach the final state, thus avoiding the need to create many layers.
Multi-view Graph Convolution for Participant Recommendation
Participant recommendation, a fundamental problem emerging together with GB, aims to find the participants for a launched group buying process with an initiator and a target item to increase the GB success rate.
Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation
The users' group participation on social platforms reveals their interests and can be utilized as side information to mitigate the data sparsity and cold-start problem in recommender systems.
DALA: A Distribution-Aware LoRA-Based Adversarial Attack against Language Models
DALA considers distribution shifts of adversarial examples to improve the attack's effectiveness under detection methods.
Large Language Models for Robotics: A Survey
Understanding and assessing this intelligence is a complex task.
Model-as-a-Service (MaaS): A Survey
MaaS is a new deployment and service paradigm for different AI-based models.
RDGCN: Reinforced Dependency Graph Convolutional Network for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) is dedicated to forecasting the sentiment polarity of aspect terms within sentences.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Aspect-based Meeting Transcript Summarization: A Two-Stage Approach with Weak Supervision on Sentence Classification
Then we merge the sentences selected for a specific aspect as the input for the summarizer to produce the aspect-based summary.
JPAVE: A Generation and Classification-based Model for Joint Product Attribute Prediction and Value Extraction
Furthermore, the copy mechanism in value generator and the value attention module in value classifier help our model address the data discrepancy issue by only focusing on the relevant part of input text and ignoring other information which causes the discrepancy issue such as sentence structure in the text.
Joint Learning of Local and Global Features for Aspect-based Sentiment Classification
Furthermore, we propose a dual-level graph attention network as a global encoder by fully employing dependency tag information to capture long-distance information effectively.
Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation
To this end, we investigate a novel problem of robust POI recommendation by considering the uncertainty factors of the user check-ins, and proposes a Bayes-enhanced Multi-view Attention Network.
Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation
Large Language Models (LLMs) can generate biased and toxic responses.
DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text
Moreover, a significant gap in the current landscape is the absence of a realistic benchmark for evaluating the effectiveness of grounding LLMs on heterogeneous knowledge sources (e. g., knowledge base and text).
Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection
While in our observation, compared to the rarity of classes, the calibrated uncertainty estimated from well-trained evidential deep learning networks better reflects model performance.
Prompt Me Up: Unleashing the Power of Alignments for Multimodal Entity and Relation Extraction
Additionally, our method is orthogonal to previous multimodal fusions, and using it on prior SOTA fusions further improves 5. 47% F1.
CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine Chain-of-Thought Prompting for Multi-domain NLU Tasks
While Chain-of-Thought prompting is popular in reasoning tasks, its application to Large Language Models (LLMs) in Natural Language Understanding (NLU) is under-explored.
Unified Pretraining for Recommendation via Task Hypergraphs
On the other hand, pretraining and finetuning on the same dataset leads to a high risk of overfitting.
Knowledge Graph Context-Enhanced Diversified Recommendation
Collectively, our contributions signify a substantial stride towards augmenting the panorama of recommendation diversity within the realm of KG-informed RecSys paradigms.
Towards Graph Foundation Models: A Survey and Beyond
Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and several other domains.
Collaborative Semantic Alignment in Recommendation Systems
However, PLMs often overlook the vital collaborative filtering signals, leading to challenges in merging collaborative and semantic representation spaces and fine-tuning semantic representations for better alignment with warm-start conditions.
AE-smnsMLC: Multi-Label Classification with Semantic Matching and Negative Label Sampling for Product Attribute Value Extraction
In this paper, we reformulate this task as a multi-label classification task that can be applied for real-world scenario in which only annotation of attribute values is available to train models (i. e., annotation of positional information of attribute values is not available).
Do Large Language Models Know about Facts?
Large language models (LLMs) have recently driven striking performance improvements across a range of natural language processing tasks.
Graph Neural Architecture Search with GPT-4
In this paper, we integrate GPT-4 into GNAS and propose a new GPT-4 based Graph Neural Architecture Search method (GPT4GNAS for short).
Discovering Utility-driven Interval Rules
For artificial intelligence, high-utility sequential rule mining (HUSRM) is a knowledge discovery method that can reveal the associations between events in the sequences.
From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
Graph-based semi-supervised learning (GSSL) has long been a hot research topic.
Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach
In this paper, we propose to incorporate the label dependencies among entity types into a multi-task learning framework for better MRC-based NER.
Localize, Retrieve and Fuse: A Generalized Framework for Free-Form Question Answering over Tables
Furthermore, TAG-QA outperforms the end-to-end model T5 by 16% and 12% on BLEU-4 and PARENT F-score, respectively.
All Labels Together: Low-shot Intent Detection with an Efficient Label Semantic Encoding Paradigm
In intent detection tasks, leveraging meaningful semantic information from intent labels can be particularly beneficial for few-shot scenarios.
Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs
In this work, we propose a novel unsupervised Skin Lesion sEgmentation framework based on structural entropy and isolation forest outlier Detection, namely SLED.
A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications
Despite this progress, there is a lack of a comprehensive overview of the attacks and the techniques for preserving privacy in the graph domain.
LLMRec: Benchmarking Large Language Models on Recommendation Task
The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation.
Graph-based Alignment and Uniformity for Recommendation
To address this issue, we propose a novel approach, graph-based alignment and uniformity (GraphAU), that explicitly considers high-order connectivities in the user-item bipartite graph.
Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning
With the proliferation of social media, a growing number of users search for and join group activities in their daily life.
Slot Induction via Pre-trained Language Model Probing and Multi-level Contrastive Learning
Recent advanced methods in Natural Language Understanding for Task-oriented Dialogue (TOD) Systems (e. g., intent detection and slot filling) require a large amount of annotated data to achieve competitive performance.
An Unforgeable Publicly Verifiable Watermark for Large Language Models
Experiments demonstrate that our algorithm attains high detection accuracy and computational efficiency through neural networks with a minimized number of parameters.
Click-Conversion Multi-Task Model with Position Bias Mitigation for Sponsored Search in eCommerce
Position bias, the phenomenon whereby users tend to focus on higher-ranked items of the search result list regardless of the actual relevance to queries, is prevailing in many ranking systems.
Enhancing Cross-lingual Transfer via Phonemic Transcription Integration
Particularly, we propose unsupervised alignment objectives to capture (1) local one-to-one alignment between the two different modalities, (2) alignment via multi-modality contexts to leverage information from additional modalities, and (3) alignment via multilingual contexts where additional bilingual dictionaries are incorporated.
A Survey on Evaluation of Large Language Models
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications.
Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering
To address this limitation, we propose Dimension Independent Mixup for Hard Negative Sampling (DINS), which is the first Area-wise sampling method for training CF-based models.
Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation
We explore the role of the fine-grained item attributes in bridging the gaps between the existing and the SCS items and pre-train a knowledgeable item-attribute graph for SCS item recommendation.
Privacy and Fairness in Federated Learning: on the Perspective of Trade-off
Federated learning (FL) has been a hot topic in recent years.
Addressing the Rank Degeneration in Sequential Recommendation via Singular Spectrum Smoothing
We also establish a correlation between the ranks of sequence and item embeddings and the rank of the user-item preference prediction matrix, which can affect recommendation diversity.
Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs).
Machine Unlearning: A Survey
Machine learning has attracted widespread attention and evolved into an enabling technology for a wide range of highly successful applications, such as intelligent computer vision, speech recognition, medical diagnosis, and more.
Inconsistent Matters: A Knowledge-guided Dual-consistency Network for Multi-modal Rumor Detection
Rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers.
Decentralized Federated Learning: A Survey and Perspective
Federated learning (FL) has been gaining attention for its ability to share knowledge while maintaining user data, protecting privacy, increasing learning efficiency, and reducing communication overhead.
GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks
These techniques neither preserve the semantic consistency of the original sentences when rule-based augmentations are adopted, nor preserve the syntax structure of sentences when expressing relations using seq2seq models, resulting in less diverse augmentations.
Give Me More Details: Improving Fact-Checking with Latent Retrieval
Evidence plays a crucial role in automated fact-checking.
Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis
Multimodal relation extraction (MRE) is the task of identifying the semantic relationships between two entities based on the context of the sentence image pair.
Enhancing Cross-lingual Natural Language Inference by Soft Prompting with Multilingual Verbalizer
In this paper, we propose a novel Soft prompt learning framework with the Multilingual Verbalizer (SoftMV) for XNLI.
Cross-Lingual Natural Language Inference
Cross-Lingual Transfer
Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs
To better comprehend the complex physical laws, this paper proposes a novel learning based simulation model- Graph Networks with Spatial-Temporal neural Ordinary Equations (GNSTODE)- that characterizes the varying spatial and temporal dependencies in particle systems using a united end-to-end framework.
Gaussian Prior Reinforcement Learning for Nested Named Entity Recognition
Existing works for nested NER ignore the recognition order and boundary position relation of nested entities.
Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training
This paper presents a novel paradigm for the Zero-Shot Item-based Recommendation (ZSIR) task, which pre-trains a model on product knowledge graph (PKG) to refine the item features from PLMs.
Contrastive Graph Clustering in Curvature Spaces
On the other hand, contrastive learning boosts the deep graph clustering but usually struggles in either graph augmentation or hard sample mining.
Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidence
In light of this, we propose a fact verification model named ReRead to retrieve evidence and verify claim that: (1) Train the evidence retriever to obtain interpretable evidence (i. e., faithfulness and plausibility criteria); (2) Train the claim verifier to revisit the evidence retrieved by the optimized evidence retriever to improve the accuracy.
Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction
Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role.
Hierarchical State Abstraction Based on Structural Information Principles
State abstraction optimizes decision-making by ignoring irrelevant environmental information in reinforcement learning with rich observations.
Conditional Denoising Diffusion for Sequential Recommendation
Generative models have attracted significant interest due to their ability to handle uncertainty by learning the inherent data distributions.
Graph Collaborative Signals Denoising and Augmentation for Recommendation
Moreover, we show that the inclusion of user-user and item-item correlations can improve recommendations for users with both abundant and insufficient interactions.
SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization
Graph Neural Networks (GNNs) are de facto solutions to structural data learning.
A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability
This paper presents the first comprehensive analysis of ChatGPT's Text-to-SQL ability.
A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT
The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster pace.
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT
This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities.
Deep Learning and Medical Imaging for COVID-19 Diagnosis: A Comprehensive Survey
In this survey, we investigate the main contributions of deep learning applications using medical images in fighting against COVID-19 from the aspects of image classification, lesion localization, and severity quantification, and review different deep learning architectures and some image preprocessing techniques for achieving a preciser diagnosis.
Weakly Supervised Anomaly Detection: A Survey
Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news.
ConsRec: Learning Consensus Behind Interactions for Group Recommendation
Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation task.
OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization
Graph Neural Networks (GNNs) are currently dominating in modeling graph-structure data, while their high reliance on graph structure for inference significantly impedes them from widespread applications.
Provable Unrestricted Adversarial Training without Compromise with Generalizability
Second, the existing AT methods often achieve adversarial robustness at the expense of standard generalizability (i. e., the accuracy on natural examples) because they make a tradeoff between them.
Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation
TA-HGAT is built in a hyperbolic space to learn the hierarchical structure of session graphs.
Self-organization Preserved Graph Structure Learning with Principle of Relevant Information
Most Graph Neural Networks follow the message-passing paradigm, assuming the observed structure depicts the ground-truth node relationships.
HUSP-SP: Faster Utility Mining on Sequence Data
High-utility sequential pattern mining (HUSPM) has emerged as an important topic due to its wide application and considerable popularity.
Towards Sequence Utility Maximization under Utility Occupancy Measure
An algorithm called Sequence Utility Maximization with Utility occupancy measure (SUMU) is proposed.
Localized Contrastive Learning on Graphs
Contrastive learning methods based on InfoNCE loss are popular in node representation learning tasks on graph-structured data.
Learning to Select from Multiple Options
To deal with the two issues, this work first proposes a contextualized TE model (Context-TE) by appending other k options as the context of the current (P, H) modeling.
Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces
On the one hand, existing methods work with the zero-curvature Euclidean space, and largely ignore the fact that curvature varies over the coming graph sequence.
DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation
Graph Neural Network (GNN) based recommender systems have been attracting more and more attention in recent years due to their excellent performance in accuracy.
MetaKRec: Collaborative Meta-Knowledge Enhanced Recommender System
To fill this gap, in this paper, we explore the rich, heterogeneous relationship among items and propose a new KG-enhanced recommendation model called Collaborative Meta-Knowledge Enhanced Recommender System (MetaKRec).
Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction
Low-Resource Information Extraction (LRIE) strives to use unsupervised data, reducing the required resources and human annotation.
Ranking-based Group Identification via Factorized Attention on Social Tripartite Graph
PA layers efficiently learn the relatedness of non-neighbor nodes to improve the information propagation to users.
Can Current Explainability Help Provide References in Clinical Notes to Support Humans Annotate Medical Codes?
The medical codes prediction problem from clinical notes has received substantial interest in the NLP community, and several recent studies have shown the state-of-the-art (SOTA) code prediction results of full-fledged deep learning-based methods.
Sequential Recommendation with Auxiliary Item Relationships via Multi-Relational Transformer
However, it remains a significant challenge to model auxiliary item relationships in SR. To simultaneously model high-order item-item transitions in sequences and auxiliary item relationships, we propose a Multi-relational Transformer capable of modeling auxiliary item relationships for SR (MT4SR).
Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks
Data augmentation techniques have been used to alleviate the problem of scarce labeled data in various NER tasks (flat, nested, and discontinuous NER tasks).
Variational Graph Generator for Multi-View Graph Clustering
The critical point of MGC is to better utilize the view-specific and view-common information in features and graphs of multiple views.
Deep Clustering: A Comprehensive Survey
Finally, we discuss the open challenges and potential future opportunities in different fields of deep clustering.
Contrast Pattern Mining: A Survey
Contrast pattern mining (CPM) is an important and popular subfield of data mining.
Totally-ordered Sequential Rules for Utility Maximization
TotalSR creates a utility table that can efficiently calculate antecedent support and a utility prefix sum list that can compute the remaining utility in O(1) time for a sequence.
Scene Graph Modification as Incremental Structure Expanding
A scene graph is a semantic representation that expresses the objects, attributes, and relationships between objects in a scene.
Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees
Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction.
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning
To bridge this gap, we make the first attempt to study the problem of self-supervised temporal graph representation learning in the general Riemannian space, supporting the time-varying curvature to shift among hyperspherical, Euclidean and hyperbolic spaces.
A Generic Algorithm for Top-K On-Shelf Utility Mining
Compared with traditional utility mining, OSUM can find more practical and meaningful patterns in real-life applications.
ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation
Then we propose Contrastive Variational AutoEncoder (ContrastVAE in short), a two-branched VAE model with contrastive regularization as an embodiment of ContrastELBO for sequential recommendation.
Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing
Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs.
Automating DBSCAN via Deep Reinforcement Learning
DBSCAN is widely used in many scientific and engineering fields because of its simplicity and practicality.
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
To bridge this gap, we present--to the best of our knowledge--the first comprehensive benchmark for unsupervised outlier node detection on static attributed graphs called BOND, with the following highlights.
Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus
To alleviate the challenges of building Knowledge Graphs (KG) from scratch, a more general task is to enrich a KG using triples from an open corpus, where the obtained triples contain noisy entities and relations.
Towards Target Sequential Rules
In this paper, we provide the relevant definitions of target sequential rule and formulate the problem of targeted sequential rule mining.
CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checking
The explosion of misinformation spreading in the media ecosystem urges for automated fact-checking.
Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination
Instead of similarity computation, GGD directly discriminates two groups of node samples with a very simple binary cross-entropy loss.
A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference
Natural Language Inference (NLI) is a growingly essential task in natural language understanding, which requires inferring the relationship between the sentence pairs (premise and hypothesis).
Evidential Temporal-aware Graph-based Social Event Detection via Dempster-Shafer Theory
To incorporate temporal information into the message passing scheme, we introduce a novel temporal-aware aggregator which assigns weights to neighbours according to an adaptive time exponential decay formula.
HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction
Unsupervised relation extraction aims to extract the relationship between entities from natural language sentences without prior information on relational scope or distribution.
XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression Extraction
To date, work in this area has mostly focused on English as there is a scarcity of labeled data for other languages.
PyGOD: A Python Library for Graph Outlier Detection
PyGOD is an open-source Python library for detecting outliers in graph data.
Multifaceted Improvements for Conversational Open-Domain Question Answering
Recently, conversational OpenQA is proposed to address these issues with the abundant contextual information in the conversation.
Improving Contrastive Learning with Model Augmentation
However, existing methods all construct views by adopting augmentation from data perspectives, while we argue that 1) optimal data augmentation methods are hard to devise, 2) data augmentation methods destroy sequential correlations, and 3) data augmentation fails to incorporate comprehensive self-supervised signals.
Deep reinforcement learning guided graph neural networks for brain network analysis
Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), enable us to model the human brain as a brain network or connectome.
G$^3$SR: Global Graph Guided Session-based Recommendation
G$^3$SR decomposes the session-based recommendation workflow into two steps.
Attend, Memorize and Generate: Towards Faithful Table-to-Text Generation in Few Shots
Few-shot table-to-text generation is a task of composing fluent and faithful sentences to convey table content using limited data.
Towards Revenue Maximization with Popular and Profitable Products
To fulfill this gap, in this paper, we first propose a general profit-oriented framework to address the problem of revenue maximization based on economic behavior, and compute the 0n-shelf Popular and most Profitable Products (OPPPs) for the targeted marketing.
TaSPM: Targeted Sequential Pattern Mining
What's more, to improve the efficiency of TaSPM on large-scale datasets and multiple-items-based sequence datasets, we propose several pruning strategies to reduce meaningless operations in mining processes.
Graph Masked Autoencoders with Transformers
In this paper, we propose Graph Masked Autoencoders (GMAEs), a self-supervised transformer-based model for learning graph representations.
Graph Neural Networks for Graphs with Heterophily: A Survey
In the end, we point out the potential directions to advance and stimulate more future research and applications on heterophilic graph learning with GNNs.
Deep learning for drug repurposing: methods, databases, and applications
Drug development is time-consuming and expensive.
Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network
We conduct a comprehensive analysis of users' online game behaviors, which motivates the necessity of handling those three characteristics in the online game recommendation.
Link Prediction with Contextualized Self-Supervision
To generate informative node embeddings for link prediction, structural context prediction is leveraged as a self-supervised learning task to boost the link prediction performance.
Dual Space Graph Contrastive Learning
In addition, we conduct extensive experiments to analyze the impact of different graph encoders on DSGC, giving insights about how to better leverage the advantages of contrastive learning between different spaces.
Sequential Recommendation via Stochastic Self-Attention
We further argue that BPR loss has no constraint on positive and sampled negative items, which misleads the optimization.
Learning from Atypical Behavior: Temporary Interest Aware Recommendation Based on Reinforcement Learning
To address this challenges, we propose a novel model called Temporary Interest Aware Recommendation (TIARec), which can distinguish atypical interactions from normal ones without supervision and capture the temporary interest as well as the general preference of users.
Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning
Meanwhile, we propose a Multi-Domain Adaptation Network (MDAN) for MSDCR to capture a user's domain-invariant aspect preference.
Interpretable and Effective Reinforcement Learning for Attacking against Graph-based Rumor Detection
To discover subtle vulnerabilities, we design a powerful attacking algorithm to camouflage rumors in social networks based on reinforcement learning that can interact with and attack any black-box detectors.
Graph Structure Learning with Variational Information Bottleneck
Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications.
An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering
Thus, graph pre-training has the great potential to alleviate data sparsity in GNN-based recommendations.
A Self-supervised Mixed-curvature Graph Neural Network
Instead of working on one single constant-curvature space, we construct a mixed-curvature space via the Cartesian product of multiple Riemannian component spaces and design hierarchical attention mechanisms for learning and fusing the representations across these component spaces.
US-Rule: Discovering Utility-driven Sequential Rules
High utility sequential pattern mining (HUSPM) is one kind of utility-driven mining.
Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network
In this paper, we propose a novel Reinforced Attentive Multi-relational Graph Neural Network (RAM-GNN) to the pre-train user and item embeddings on the user and item graph prior to the recommendation step.
Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting
However, this approach failed to explicitly reflect the correlations between different nodes at different time steps, thus limiting the learning capability of graph neural networks.
Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation
Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs.
Federated Social Recommendation with Graph Neural Network
However, they all require centralized storage of the social links and item interactions of users, which leads to privacy concerns.
Pre-training Graph Neural Network for Cross Domain Recommendation
Then, we transfer the pre-trained graph encoder to initialize the node embeddings on the target domain, which benefits the fine-tuning of the single domain recommender system on the target domain.
CvS: Classification via Segmentation For Small Datasets
The success of deep learning methods relies heavily on the availability of a large amount of data.
Dense Hierarchical Retrieval for Open-Domain Question Answering
In this work, we propose Dense Hierarchical Retrieval (DHR), a hierarchical framework that can generate accurate dense representations of passages by utilizing both macroscopic semantics in the document and microscopic semantics specific to each passage.
ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network
Hyperbolic Graph Neural Networks(HGNNs) extend GNNs to hyperbolic space and thus are more effective to capture the hierarchical structures of graphs in node representation learning.
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning
While pre-training and meta-training can create deep models powerful for few-shot generalization, we find that pre-training and meta-training focuses respectively on cross-domain transferability and cross-task transferability, which restricts their data efficiency in the entangled settings of domain shift and task shift.
Cross-lingual COVID-19 Fake News Detection
The COVID-19 pandemic poses a great threat to global public health.
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization
To capture the semantic graph structure from raw text, most existing summarization approaches are built on GNNs with a pre-trained model.
Deep Fraud Detection on Non-attributed Graph
The successes of most previous methods heavily rely on rich node features and high-fidelity labels.
Self-supervised Learning for Sequential Recommendation with Model Augmentation
However, existing methods all construct views by adopting augmentation from data perspectives, while we argue that 1) optimal data augmentation methods are hard to devise, 2) data augmentation methods destroy sequential correlations, and 3) data augmentation fails to incorporate comprehensive self-supervised signals.
ESCo: Towards Provably Effective and Scalable Contrastive Representation Learning
In this paper, we propose ESCo (Effective and Scalable Contrastive), a new contrastive framework which is essentially an instantiation of the Information Bottleneck principle under self-supervised learning settings.
Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction
Low-resource Relation Extraction (LRE) aims to extract relation facts from limited labeled corpora when human annotation is scarce.
Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation
They utilize simple and fixed schemes, like neighborhood information aggregation or mathematical calculation of vectors, to fuse the embeddings of different user behaviors to obtain a unified embedding to represent a user's behavioral patterns which will be used in downstream recommendation tasks.
DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN
In the information explosion era, recommender systems (RSs) are widely studied and applied to discover user-preferred information.
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer
Therefore, we propose to unify sequential patterns and temporal collaborative signals to improve the quality of recommendation, which is rather challenging.
Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
In this paper, we investigate the application of contrastive Self-Supervised Learning (SSL) to the sequential recommendation, as a way to alleviate some of these issues.
A Survey on Deep Learning Event Extraction: Approaches and Applications
Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.
Dual Adversarial Variational Embedding for Robust Recommendation
First, the noise injection based methods often draw the noise from a fixed noise distribution given in advance, while in real world, the noise distributions of different users and items may differ from each other due to personal behaviors and item usage patterns.
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks
We introduce a conceptually simple yet effective model for self-supervised representation learning with graph data.
Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations
Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually.
Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation
We use Elliptical Gaussian distributions to describe items and sequences with uncertainty.
Sketch-Based Anomaly Detection in Streaming Graphs
This higher-order sketch has the useful property of preserving the dense subgraph structure (dense subgraphs in the input turn into dense submatrices in the data structure).
Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection
Pre-trained Transformer-based models were reported to be robust in intent classification.
A Comprehensive Survey on Community Detection with Deep Learning
A community reveals the features and connections of its members that are different from those in other communities in a network.
A Robust and Generalized Framework for Adversarial Graph Embedding
With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-quality graph embedding vectors various types of graphs.
Graph Learning based Recommender Systems: A Review
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS).
Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks
Based on them, we design MinGE to directly calculate the ideal node embedding dimension for any graph.
Error-Robust Multi-View Clustering: Progress, Challenges and Opportunities
Due to inevitable sensor failures, data in each view may contain error.
Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer
We firstly pre-train a transformer with sequences in a reverse direction to predict prior items.
User Preference-aware Fake News Detection
The majority of existing fake news detection algorithms focus on mining news content and/or the surrounding exogenous context for discovering deceptive signals; while the endogenous preference of a user when he/she decides to spread a piece of fake news or not is ignored.
Ranked #1 on
Graph Classification
on UPFD-GOS
Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks
To avoid the embedding over-assimilation among different types of nodes, we employ a label-aware neural similarity measure to ascertain the most similar neighbors based on node attributes.
Ranked #3 on
Node Classification
on Amazon-Fraud
Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks
Furthermore, they cannot fully capture the content-based correlations between nodes, as they either do not use the self-attention mechanism or only use it to consider the immediate neighbors of each node, ignoring the higher-order neighbors.
Membership Inference Attacks on Knowledge Graphs
Membership inference attacks (MIAs) infer whether a specific data record is used for target model training.
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support.
HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization
The second one encourages the structure encoder to learn better representations with desired characteristics for all labels which can better handle label imbalance in hierarchical text classification.
Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs
To model the uncertainty, we devise a hyperbolic graph variational autoencoder built upon the proposed TGNN to generate stochastic node representations of hyperbolic normal distributions.
Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks
Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method.
I-ODA, Real-World Multi-modal Longitudinal Data for OphthalmicApplications
In this paper, we present a new multi-modal longitudinal ophthalmic imaging dataset, the Illinois Ophthalmic Database Atlas (I-ODA), with the goal of advancing state-of-the-art computer vision applications in ophthalmology, and improving upon the translatable capacity of AI based applications across different clinical settings.
An Introduction to Robust Graph Convolutional Networks
In this paper, we propose a novel Robust Graph Convolutional Neural Networks for possible erroneous single-view or multi-view data where data may come from multiple sources.
PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning
This paper models these structures by presenting PredRNN, a new recurrent network, in which a pair of memory cells are explicitly decoupled, operate in nearly independent transition manners, and finally form unified representations of the complex environment.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
Membership Inference Attacks on Machine Learning: A Survey
In recent years, MIAs have been shown to be effective on various ML models, e. g., classification models and generative models.
