3 research outputs found
Structure-Preserving Community In A Multilayer Network: Definition, Detection, And Analysis
Multilayer networks or MLNs (also called multiplexes or network of networks)
are being used extensively for modeling and analysis of data sets with multiple
entity and feature types as well as their relationships. As the concept of
communities and hubs are used for these analysis, a structure-preserving
definition for them on MLNs (that retains the original MLN structure and
node/edge labels and types) and its efficient detection are critical. There is
no structure-preserving definition of a community for a MLN as most of the
current analyses aggregate a MLN to a single graph. Although there is consensus
on community definition for single graphs (and detection packages) and to a
lesser extent for homogeneous MLNs, it is lacking for heterogeneous MLNs. In
this paper, we not only provide a structure-preserving definition for the first
time, but also its efficient computation using a decoupling approach, and
discuss its characteristics & significance for analysis. The proposed
decoupling approach for efficiency combines communities from individual layers
to form a serial k-community for connected k layers in a MLN. We propose
several weight metrics for composing layer-wise communities using the bipartite
graph match approach based on the analysis semantics. Our proposed approach has
a number of advantages. It: i) leverages extant single graph community
detection algorithms, ii) is based on the widely-used maximal flow bipartite
graph matching for composing k layers, iii) introduces several weight metrics
that are customized for the community concept, and iv) experimentally validates
the definition, mapping, and efficiency from a flexible analysis perspective on
widely-used IMDb data set.
Keywords: Heterogeneous Multilayer Networks; Bipartite Graphs; Community
Definition and Detection; Decoupling-Based CompositionComment: 27 pages, Submitted to VLDB 201
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 networks)
are being used extensively for modeling and analysis of data sets with multiple
entity and feature types and associated relationships. Although the concept of
community is widely-used for aggregate analysis, a structure- and semantics
preserving definition for it is lacking for MLNs. Retention of original MLN
structure and entity relationships is important for detailed drill-down
analysis. In addition, efficient computation is also critical for large number
of analysis.
In this paper, we introduce a structure-preserving community definition for
MLNs as well as a framework for its efficient computation using the decoupling
approach. The proposed decoupling approach combines communities from individual
layers to form a serial k-community for connected k layers in a MLN. We propose
a new algorithm for pairing communities across layers and introduce several
weight metrics for composing communities from two layers using participating
community characteristics. In addition to the definition, our proposed approach
has a number of desired characteristics. It: i) leverages extant single graph
community detection algorithms, ii) introduces several weight metrics that are
customized for the community concept, iii) is a new algorithm for pairing
communities using bipartite graphs, and iv) experimentally validates the
community computation and its efficiency on widely-used IMDb and DBLP data
sets.Comment: arXiv admin note: substantial text overlap with arXiv:1903.0264
A New Community Definition For MultiLayer Networks And A Novel Approach For Its Efficient Computation
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.Comment: arXiv admin note: substantial text overlap with arXiv:1910.01737,
arXiv:1903.0264