
TL;DR
This paper introduces a novel continuous optimization approach for detecting network modules by maximizing a multilinear polynomial potential function, enabling more flexible and effective graph clustering strategies.
Contribution
It proposes a new multilinear polynomial objective function for graph clustering, transforming the discrete problem into a continuous one for improved search strategies.
Findings
Potential function maximization yields clear module partitions.
Continuous framework allows alternative search strategies.
Comparison shows advantages over traditional greedy methods.
Abstract
When analyzing complex networks a key target is to uncover their modular structure, which means searching for a family of modules, namely node subsets spanning each a subnetwork more densely connected than the average. This work proposes a novel type of objective function for graph clustering, in the form of a multilinear polynomial whose coefficients are determined by network topology. It may be thought of as a potential function, to be maximized, taking its values on fuzzy clusterings or families of fuzzy subsets of nodes over which every node distributes a unit membership. When suitably parametrized, this potential is shown to attain its maximum when every node concentrates its all unit membership on some module. The output thus is a partition, while the original discrete optimization problem is turned into a continuous version allowing to conceive alternative search strategies. The…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComplex Network Analysis Techniques · Graph theory and applications · Topological and Geometric Data Analysis
