Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts

Abstract

Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph. But can nodes really be best described by a single vector representation? In this work, we propose a method for learning multiple representations of the nodes in a graph (e.g., the users of a social network). Based on a principled decomposition of the ego-network, each representation encodes the role of the node in a different local community in which the nodes participate. These representations allow for improved reconstruction of the nuanced relationships that occur in the graph -- a phenomenon that we illustrate through state-of-the-art results on link prediction tasks on a variety of graphs, reducing the error by up to $90\%$. In addition, we show that these embeddings allow for effective visual analysis of the learned community structure.

References in corpus (6)

• Fast unfolding of communities in large networks
• LINE: Large-scale Information Network Embedding
• Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities
• HARP: Hierarchical Representation Learning for Networks
• Empirical Comparison of Algorithms for Network Community Detection
• DDGK: Learning Graph Representations for Deep Divergence Graph Kernels

Cited by in corpus (16)

• PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest
• DDGK: Learning Graph Representations for Deep Divergence Graph Kernels
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• Graph Neighborhood Attentive Pooling
• Which way? Direction-Aware Attributed Graph Embedding
• Friendship is All we Need: A Multi-graph Embedding Approach for Modeling Customer Behavior
• Explainable Recommender Systems via Resolving Learning Representations
• Multi-Aspect Temporal Network Embedding: A Mixture of Hawkes Process View
• Bot-Match: Social Bot Detection with Recursive Nearest Neighbors Search
• Multi-Facet Recommender Networks with Spherical Optimization
• Pair-view Unsupervised Graph Representation Learning
• mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network Embedding
• Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model
• Graph Embedding with Hierarchical Attentive Membership
• HNet: Graphical Hypergeometric Networks