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Found 19 papers, 12 papers with code
TransFlower: An Explainable Transformer-Based Model with Flow-to-Flow Attention for Commuting Flow Prediction
Understanding the link between urban planning and commuting flows is crucial for guiding urban development and policymaking.
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation
In this paper, we base on the phenomenon and propose a general and novel research problem of generalized graph anomaly detection that aims to effectively identify anomalies on both the training-domain graph and unseen testing graph to eliminate potential dangers.
Timestamps as Prompts for Geography-Aware Location Recommendation
In this paper, we revisit the problem of location recommendation and point out that explicitly modeling temporal information is a great help when the model needs to predict not only the next location but also further locations.
End-to-End Personalized Next Location Recommendation via Contrastive User Preference Modeling
History encoder is designed to model mobility patterns from historical check-in sequences, while query generator explicitly learns user preferences to generate user-specific intention queries.
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation
In this paper, we base on the phenomenon and propose a general and novel research problem of generalized graph anomaly detection that aims to effectively identify anomalies on both the training-domain graph and unseen testing graph to eliminate potential dangers.
Unseen Anomaly Detection on Networks via Multi-Hypersphere Learning
Network anomaly detection is a crucial task since a few anomalies can cause huge losses.
Subtractive Aggregation for Attributed Network Anomaly Detection
It aims to detect nodes that significantly deviate from their corresponding background.
Adaptation-Agnostic Meta-Training
Many meta-learning algorithms can be formulated into an interleaved process, in the sense that task-specific predictors are learned during inner-task adaptation and meta-parameters are updated during meta-update.
A Closer Look at the Training Strategy for Modern Meta-Learning
The support/query (S/Q) episodic training strategy has been widely used in modern meta-learning algorithms and is believed to improve their generalization ability to test environments.
Against Adversarial Learning: Naturally Distinguish Known and Unknown in Open Set Domain Adaptation
Open set domain adaptation refers to the scenario that the target domain contains categories that do not exist in the source domain.
Mixed Set Domain Adaptation
Under the settings of MSDA, different categories of the source dataset are not all collected from the same domain(s).
Deep Adversarial Domain Adaptation Based on Multi-layer Joint Kernelized Distance
In this paper, a deep adversarial domain adaptation model based on a multi-layer joint kernelized distance metric is proposed.
Network Together: Node Classification via Cross-Network Deep Network Embedding
On the other hand, node attributes and labels are leveraged to capture the proximities between nodes across different networks by making the same labeled nodes across networks have aligned latent vector representations.
Adversarial Deep Network Embedding for Cross-network Node Classification
This motivates us to propose an adversarial cross-network deep network embedding (ACDNE) model to integrate adversarial domain adaptation with deep network embedding so as to learn network-invariant node representations that can also well preserve the network structural information.
Variational Metric Scaling for Metric-Based Meta-Learning
In this paper, we recast metric-based meta-learning from a Bayesian perspective and develop a variational metric scaling framework for learning a proper metric scaling parameter.
Network Together: Node Classification via Cross network Deep Network Embedding
On the other hand, node attributes and labels are leveraged to capture the proximities between nodes across different networks by making the same labeled nodes across networks have aligned latent vector representations.
Social and Information Networks
Deep Network Embedding for Graph Representation Learning in Signed Networks
As an effective approach to solve graph mining problems, network embedding aims to learn a low-dimensional feature vector representation for each node of a given network.
Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning
Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically follow different distributions.
Transfer Prototype-based Fuzzy Clustering
Specifically, the idea of leveraging knowledge from the source domain is exploited to develop a set of transfer prototype based fuzzy clustering (TPFC) algorithms.
