A New Community Definition For MultiLayer Networks And A Novel Approach For Its Efficient Computation

by   Abhishek Santra, et al.

As the use of MultiLayer Networks (or MLNs) for modeling and analysis is gaining popularity, it is becoming increasingly important to propose a community definition that encompasses the multiple features represented by MLNs and develop algorithms for efficiently computing communities on MLNs. Currently, communities for MLNs, are based on aggregating the networks into single graphs using different techniques (type independent, projection-based, etc.) and applying single graph community detection algorithms, such as Louvain and Infomap on these graphs. This process results in different types of information loss (semantics and structure). To the best of our knowledge, in this paper we propose, for the first time, a definition of community for heterogeneous MLNs (or HeMLNs) which preserves semantics as well as the structure. Additionally, our basic definition can be extended to appropriately match the analysis objectives as needed. In this paper, we present a structure and semantics preserving community definition for HeMLNs that is compatible with and is an extension of the traditional definition for single graphs. We also present a framework for its efficient computation using a newly proposed decoupling approach. First, we define a k-community for connected k layers of a HeMLN. Then we propose a family of algorithms for its computation using the concept of bipartite graph pairings. Further, for a broader analysis, we introduce several pairing algorithms and weight metrics for composing binary HeMLN communities using participating community characteristics. Essentially, this results in an extensible family of community computations. We provide extensive experimental results for showcasing the efficiency and analysis flexibility of the proposed computation using popular IMDb and DBLP data sets.



There are no comments yet.


page 3

page 12

page 13


An Efficient Framework for Computing Structure- And Semantics-Preserving Community in a Heterogeneous Multilayer Network

Multilayer networks or MLNs (also called multiplexes or network of netwo...

Structure-Preserving Community In A Multilayer Network: Definition, Detection, And Analysis

Multilayer networks or MLNs (also called multiplexes or network of netwo...

Making a Case for MLNs for Data-Driven Analysis: Modeling, Efficiency, and Versatility

Datasets of real-world applications are characterized by entities of dif...

From Base Data To Knowledge Discovery – A Life Cycle Approach – Using Multilayer Networks

Any large complex data analysis to infer or discover meaningful informat...

Efficient Community Detection in Boolean Composed Multiplex Networks

Networks (or graphs) are used to model the dyadic relations between enti...

Graphs without 2-community structures

In the context of community structure detection, we study the existence ...

Multilayer Clustered Graph Learning

Multilayer graphs are appealing mathematical tools for modeling multiple...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.