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Found 24 papers, 12 papers with code
An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor Isomorphism
Entity alignment (EA) aims to discover the equivalent entity pairs between KGs, which is a crucial step for integrating multi-source KGs. For a long time, most researchers have regarded EA as a pure graph representation learning task and focused on improving graph encoders while paying little attention to the decoding process. In this paper, we propose an effective and efficient EA Decoding Algorithm via Third-order Tensor Isomorphism (DATTI). Specifically, we derive two sets of isomorphism equations: (1) Adjacency tensor isomorphism equations and (2) Gramian tensor isomorphism equations. By combining these equations, DATTI could effectively utilize the adjacency and inner correlation isomorphisms of KGs to enhance the decoding process of EA. Extensive experiments on public datasets indicate that our decoding algorithm can deliver significant performance improvements even on the most advanced EA methods, while the extra required time is less than 3 seconds.
KwaiAgents: Generalized Information-seeking Agent System with Large Language Models
Driven by curiosity, humans have continually sought to explore and understand the world around them, leading to the invention of various tools to satiate this inquisitiveness.
NeutronOrch: Rethinking Sample-based GNN Training under CPU-GPU Heterogeneous Environments
After extensive experiments and analysis, we find that existing task orchestrating methods fail to fully utilize the heterogeneous resources, limited by inefficient CPU processing or GPU resource contention.
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective
Many Graph Neural Network (GNN) training systems have emerged recently to support efficient GNN training.
A Simple Temporal Information Matching Mechanism for Entity Alignment Between Temporal Knowledge Graphs
However, we believe that it is not necessary to learn the embeddings of temporal information in KGs since most TKGs have uniform temporal representations.
FlowX: Towards Explainable Graph Neural Networks via Message Flows
We investigate the explainability of graph neural networks (GNNs) as a step toward elucidating their working mechanisms.
A Dual-Attention Neural Network for Pun Location and Using Pun-Gloss Pairs for Interpretation
Pun location is to identify the punning word (usually a word or a phrase that makes the text ambiguous) in a given short text, and pun interpretation is to find out two different meanings of the punning word.
Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
We employ our methods to participate in the 2021 KDD Cup on OGB Large-Scale Challenge (OGB-LSC), which aims to predict the HOMO-LUMO energy gap of molecules.
Evaluation of Transportation Systems and Novel UV-Oriented Solution for Integration, Resilience, Inclusiveness and Sustainability
ransportation systems have revolutionized the form of society.
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning.
On Explainability of Graph Neural Networks via Subgraph Explorations
To make the tree search more effective, we propose to use Shapley values as a measure of subgraph importance, which can also capture the interactions among different subgraphs.
Semi-discrete and fully discrete mixed finite element methods for Maxwell viscoelastic model of wave propagation
Semi-discrete and fully discrete mixed finite element methods are considered for Maxwell-model-based problems of wave propagation in linear viscoelastic solid.
Numerical Analysis Numerical Analysis
Node2Seq: Towards Trainable Convolutions in Graph Neural Networks
Several graph neural network approaches are proposed for node feature learning and they generally follow a neighboring information aggregation scheme to learn node features.
Explainability in Graph Neural Networks: A Taxonomic Survey
To facilitate evaluations, we generate a set of benchmark graph datasets specifically for GNN explainability.
Towards Improved and Interpretable Deep Metric Learning via Attentive Grouping
Grouping has been commonly used in deep metric learning for computing diverse features.
Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents
Moreover, a powerful satisfaction model can be used as an objective function that a conversational agent continuously optimizes for.
Deep Learning of High-Order Interactions for Protein Interface Prediction
However, these methods do not incorporate the important sequential information from amino acid chains and the high-order pairwise interactions.
Protein Interface Prediction
Vocal Bursts Intensity Prediction
XGNN: Towards Model-Level Explanations of Graph Neural Networks
Furthermore, our experimental results indicate that the generated graphs can provide guidance on how to improve the trained GNNs.
Large-scale Hybrid Approach for Predicting User Satisfaction with Conversational Agents
An widely used approach to tackle this is to collect human annotation data and use them for evaluation or modeling.
StructPool: Structured Graph Pooling via Conditional Random Fields
Learning high-level representations for graphs is of great importance for graph analysis tasks.
XFake: Explainable Fake News Detector with Visualizations
In this demo paper, we present the XFake system, an explainable fake news detector that assists end-users to identify news credibility.
Global Pixel Transformers for Virtual Staining of Microscopy Images
It is also shown that our proposed global pixel transformer layer is useful to improve the fluorescence image prediction results.
Pixel Deconvolutional Networks
When used in image generation tasks, our PixelDCL can largely overcome the checkerboard problem suffered by regular deconvolution operations.
Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions
In this work, we propose spatial VAEs that use feature maps of larger size as latent variables to explicitly capture spatial information.
