Decoupled Variational Embedding for Signed Directed Networks

by   Xu Chen, et al.

Node representation learning for signed directed networks has received considerable attention in many real-world applications such as link sign prediction, node classification and node recommendation. The challenge lies in how to adequately encode the complex topological information of the networks. Recent studies mainly focus on preserving the first-order network topology which indicates the closeness relationships of nodes. However, these methods generally fail to capture the high-order topology which indicates the local structures of nodes and serves as an essential characteristic of the network topology. In addition, for the first-order topology, the additional value of non-existent links is largely ignored. In this paper, we propose to learn more representative node embeddings by simultaneously capturing the first-order and high-order topology in signed directed networks. In particular, we reformulate the representation learning problem on signed directed networks from a variational auto-encoding perspective and further develop a decoupled variational embedding (DVE) method. DVE leverages a specially designed auto-encoder structure to capture both the first-order and high-order topology of signed directed networks, and thus learns more representative node embedding. Extensive experiments are conducted on three widely used real-world datasets. Comprehensive results on both link sign prediction and node recommendation task demonstrate the effectiveness of DVE. Qualitative results and analysis are also given to provide a better understanding of DVE.


page 3

page 20

page 22

page 23


Learning Topological Representation for Networks via Hierarchical Sampling

The topological information is essential for studying the relationship b...

Deep Feature Learning of Multi-Network Topology for Node Classification

Networks are ubiquitous structure that describes complex relationships b...

GloDyNE: Global Topology Preserving Dynamic Network Embedding

Learning low-dimensional topological representation of a network in dyna...

Dual Graph Embedding for Object-Tag LinkPrediction on the Knowledge Graph

Knowledge graphs (KGs) composed of users, objects, and tags are widely u...

A Signed Subgraph Encoding Approach via Linear Optimization for Link Sign Prediction

In this paper, we consider the problem of inferring the sign of a link b...

Transtemporal edges and crosslayer edges in incompressible high-order networks

This work presents some outcomes of a theoretical investigation of incom...

TME-BNA: Temporal Motif-Preserving Network Embedding with Bicomponent Neighbor Aggregation

Evolving temporal networks serve as the abstractions of many real-life d...

Please sign up or login with your details

Forgot password? Click here to reset