Node, motif and subgraph: Leveraging network functional blocks through structural convolution

Carl Yang, Mengxiong Liu, Vincent W. Zheng, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Networks or graphs provide a natural and generic way for modeling rich structured data. Recent research on graph analysis has been focused on representation learning, of which the goal is to encode the network structures into distributed embedding vectors, so as to enable various downstream applications through off-the-shelf machine learning. However, existing methods mostly focus on node-level embedding, which is insufficient for subgraph analysis. Moreover, their leverage of network structures through path sampling or neighborhood preserving is implicit and coarse. Network motifs allow graph analysis in a finer granularity, but existing methods based on motif matching are limited to enumerated simple motifs and do not leverage node labels and supervision. In this paper, we develop NEST, a novel hierarchical network embedding method combining motif filtering and convolutional neural networks. Motif-based filtering enables NEST to capture exact small structures within networks, and convolution over the filtered embedding allows it to fully explore complex substructures and their combinations. NEST can be trivially applied to any domain and provide insight into particular network functional blocks. Extensive experiments on protein function prediction, drug toxicity prediction and social network community identification have demonstrated its effectiveness and efficiency.

Original languageEnglish (US)
Title of host publicationProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
EditorsAndrea Tagarelli, Chandan Reddy, Ulrik Brandes
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages47-52
Number of pages6
ISBN (Electronic)9781538660515
DOIs
StatePublished - Oct 24 2018
Event10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 - Barcelona, Spain
Duration: Aug 28 2018Aug 31 2018

Publication series

NameProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018

Other

Other10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
CountrySpain
CityBarcelona
Period8/28/188/31/18

ASJC Scopus subject areas

  • Sociology and Political Science
  • Communication
  • Computer Networks and Communications
  • Information Systems and Management

Fingerprint Dive into the research topics of 'Node, motif and subgraph: Leveraging network functional blocks through structural convolution'. Together they form a unique fingerprint.

Cite this