4 research outputs found
PersonaSAGE: A Multi-Persona Graph Neural Network
Graph Neural Networks (GNNs) have become increasingly important in recent
years due to their state-of-the-art performance on many important downstream
applications. Existing GNNs have mostly focused on learning a single node
representation, despite that a node often exhibits polysemous behavior in
different contexts. In this work, we develop a persona-based graph neural
network framework called PersonaSAGE that learns multiple persona-based
embeddings for each node in the graph. Such disentangled representations are
more interpretable and useful than a single embedding. Furthermore, PersonaSAGE
learns the appropriate set of persona embeddings for each node in the graph,
and every node can have a different number of assigned persona embeddings. The
framework is flexible enough and the general design helps in the wide
applicability of the learned embeddings to suit the domain. We utilize publicly
available benchmark datasets to evaluate our approach and against a variety of
baselines. The experiments demonstrate the effectiveness of PersonaSAGE for a
variety of important tasks including link prediction where we achieve an
average gain of 15% while remaining competitive for node classification.
Finally, we also demonstrate the utility of PersonaSAGE with a case study for
personalized recommendation of different entity types in a data management
platform.Comment: 10 pages, 6 figures, 7 table
CGC: Contrastive Graph Clustering for Community Detection and Tracking
Given entities and their interactions in the web data, which may have
occurred at different time, how can we find communities of entities and track
their evolution? In this paper, we approach this important task from graph
clustering perspective. Recently, state-of-the-art clustering performance in
various domains has been achieved by deep clustering methods. Especially, deep
graph clustering (DGC) methods have successfully extended deep clustering to
graph-structured data by learning node representations and cluster assignments
in a joint optimization framework. Despite some differences in modeling choices
(e.g., encoder architectures), existing DGC methods are mainly based on
autoencoders and use the same clustering objective with relatively minor
adaptations. Also, while many real-world graphs are dynamic, previous DGC
methods considered only static graphs. In this work, we develop CGC, a novel
end-to-end framework for graph clustering, which fundamentally differs from
existing methods. CGC learns node embeddings and cluster assignments in a
contrastive graph learning framework, where positive and negative samples are
carefully selected in a multi-level scheme such that they reflect hierarchical
community structures and network homophily. Also, we extend CGC for
time-evolving data, where temporal graph clustering is performed in an
incremental learning fashion, with the ability to detect change points.
Extensive evaluation on real-world graphs demonstrates that the proposed CGC
consistently outperforms existing methods.Comment: TheWebConf 2022 Research Trac