An Algorithm of Inductively Identifying Clusters from Attributed Graphs

Document Type

Article

Publication Date

4-1-2022

Abstract

Attributed graphs are widely used to represent network data where the attribute information of nodes is available. To address the problem of identifying clusters in attributed graphs, most of existing solutions are developed simply based on certain particular assumptions related to the characteristics of clusters of interest. However, it is yet unknown whether such assumed characteristics are consistent with attributed graphs. To overcome this issue, we innovatively introduce an inductive clustering algorithm that tends to address the clustering problem for attributed graphs without any assumption made on the clusters. To do so, we first process the attribute information to obtain pairwise attribute values that significantly frequently co-occur in adjacent nodes as we believe that they have potential ability to represent the characteristics of a given attributed graph. For two adjacent nodes, their likelihood of being grouped in the same cluster can be weighted by their ability to characterize the graph. Then based on these verifed characteristics instead of assumed ones, a depth-first search strategy is applied to perform the clustering task. Moreover, we are able to classify clusters such that their significance can be indicated. The experimental results demonstrate the performance and usefulness of our algorithm.

Identifier

85087810451 (Scopus)

Publication Title

IEEE Transactions on Big Data

External Full Text Location

https://doi.org/10.1109/TBDATA.2020.2964544

e-ISSN

23327790

First Page

523

Last Page

534

Issue

2

Volume

8

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