A survey of community detection approaches: From statistical modeling to deep learning
Community detection, a fundamental task for network analysis, aims to partition a network
into multiple sub-structures to help reveal their latent functions. Community detection has …
into multiple sub-structures to help reveal their latent functions. Community detection has …
Link prediction techniques, applications, and performance: A survey
Link prediction finds missing links (in static networks) or predicts the likelihood of future links
(in dynamic networks). The latter definition is useful in network evolution (Wang et al., 2011; …
(in dynamic networks). The latter definition is useful in network evolution (Wang et al., 2011; …
Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
Many widely used datasets for graph machine learning tasks have generally been
homophilous, where nodes with similar labels connect to each other. Recently, new Graph …
homophilous, where nodes with similar labels connect to each other. Recently, new Graph …
ROLAND: graph learning framework for dynamic graphs
Graph Neural Networks (GNNs) have been successfully applied to many real-world static
graphs. However, the success of static graphs has not fully translated to dynamic graphs due …
graphs. However, the success of static graphs has not fully translated to dynamic graphs due …
Parameterized explainer for graph neural network
Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by
GNNs remains a challenging open problem. The leading method mainly addresses the local …
GNNs remains a challenging open problem. The leading method mainly addresses the local …
Gcc: Graph contrastive coding for graph neural network pre-training
Graph representation learning has emerged as a powerful technique for addressing real-
world problems. Various downstream graph learning tasks have benefited from its recent …
world problems. Various downstream graph learning tasks have benefited from its recent …
Evaluating post-hoc explanations for graph neural networks via robustness analysis
This work studies the evaluation of explaining graph neural networks (GNNs), which is
crucial to the credibility of post-hoc explainability in practical usage. Conventional evaluation …
crucial to the credibility of post-hoc explainability in practical usage. Conventional evaluation …
Diffusion improves graph learning
J Gasteiger, S Weißenberger… - Advances in neural …, 2019 - proceedings.neurips.cc
Graph convolution is the core of most Graph Neural Networks (GNNs) and usually
approximated by message passing between direct (one-hop) neighbors. In this work, we …
approximated by message passing between direct (one-hop) neighbors. In this work, we …
Federated learning on non-iid graphs via structural knowledge sharing
Graph neural networks (GNNs) have shown their superiority in modeling graph data. Owing
to the advantages of federated learning, federated graph learning (FGL) enables clients to …
to the advantages of federated learning, federated graph learning (FGL) enables clients to …
A survey of Twitter research: Data model, graph structure, sentiment analysis and attacks
Twitter is the third most popular worldwide Online Social Network (OSN) after Facebook and
Instagram. Compared to other OSNs, it has a simple data model and a straightforward data …
Instagram. Compared to other OSNs, it has a simple data model and a straightforward data …