A mixed-integer linear programming approach for soft graph clustering

06/11/2019
by   Vicky Mak-Hau, et al.
0

This paper proposes a Mixed-Integer Linear Programming approach for the Soft Graph Clustering Problem. This is the first method that simultaneously allocates membership proportion for vertices that lie in multiple clusters, and that enforces an equal balance of the cluster memberships. Compared to ([Palla et al., 2005], [Derenyi et al., 2005], [Adamcsek et al., 2006]), the clusters found in our method are not limited to k-clique neighbourhoods. Compared to ([Hope and Keller, 2013]), our method can produce non-trivial clusters even for a connected unweighted graph.

READ FULL TEXT
research
01/30/2019

Determining r- and (r,s)-Robustness of Digraphs Using Mixed Integer Linear Programming

There has been an increase in the use of resilient control algorithms ba...
research
02/19/2021

Information-Theoretic Abstractions for Resource-Constrained Agents via Mixed-Integer Linear Programming

In this paper, a mixed-integer linear programming formulation for the pr...
research
11/27/2016

Verifying Integer Programming Results

Software for mixed-integer linear programming can return incorrect resul...
research
05/22/2017

Size Matters: Cardinality-Constrained Clustering and Outlier Detection via Conic Optimization

Plain vanilla K-means clustering is prone to produce unbalanced clusters...
research
06/18/2018

Overlapping Clustering Models, and One (class) SVM to Bind Them All

People belong to multiple communities, words belong to multiple topics, ...
research
02/19/2013

Breaking the Small Cluster Barrier of Graph Clustering

This paper investigates graph clustering in the planted cluster model in...
research
01/02/2023

Lagrangian Relaxation for Mixed-Integer Linear Programming: Importance, Challenges, Recent Advancements, and Opportunities

Operations in areas of importance to society are frequently modeled as M...

Please sign up or login with your details

Forgot password? Click here to reset