Learning Role-based Graph Embeddings
Random walks are at the heart of many existing network embedding methods. However, such algorithms have many limitations that arise from the use of random walks, e.g., the features resulting from these methods are unable to transfer to new nodes and graphs as they are tied to vertex identity. In this work, we introduce the Role2Vec framework which uses the flexible notion of attributed random walks, and serves as a basis for generalizing existing methods such as DeepWalk, node2vec, and many others that leverage random walks. Our proposed framework enables these methods to be more widely applicable for both transductive and inductive learning as well as for use on graphs with attributes (if available). This is achieved by learning functions that generalize to new nodes and graphs. We show that our proposed framework is effective with an average AUC improvement of 16.55% while requiring on average 853x less space than existing methods on a variety of graphs.
Code (2)
Tasks
Inductive LearningNetwork EmbeddingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Role action embeddings: scalable representation of network positions
We consider the question of embedding nodes with similar local neighborhoods together in embedding space, commonly referred to as "role embeddings." We propose RAE, an unsupervised framework that learns role embeddings. …
General ClassificationGraph Neural NetworkNode ClassificationTemporal Analysis of Reddit Networks via Role Embeddings
Inspired by diachronic word analysis from the field of natural language processing, we propose an approach for uncovering temporal insights regarding user roles from social networks using graph embedding methods. Specifi…
Graph EmbeddingRole EmbeddingOn Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications
Structural roles define sets of structurally similar nodes that are more similar to nodes inside the set than outside, whereas communities define sets of nodes with more connections inside the set than outside. Roles bas…
MisconceptionsNetwork EmbeddingLearning Structural Node Embeddings Via Diffusion Wavelets
Nodes residing in different parts of a graph can have similar structural roles within their local network topology. The identification of such roles provides key insight into the organization of networks and can be used …
Learning node embeddings via summary graphs: a brief theoretical analysis
Graph representation learning plays an important role in many graph mining applications, but learning embeddings of large-scale graphs remains a problem. Recent works try to improve scalability via graph summarization --…
Graph MiningGraph Representation LearningRepresentation Learning