A Method for Solving a Bipartite-Graph Clustering Problem with Sequence Optimization

2007 ◽  
Author(s):  
Keiu Harada ◽  
Takuya Ishioka ◽  
Ikuo Suzuki ◽  
Masashi Furukawa
Keyword(s):  
Bipartite Graph ◽  
Graph Clustering ◽  
Sequence Optimization ◽  
Clustering Problem

A local search for a graph clustering problem

2016 ◽  
Author(s):  
Anna Navrotskaya ◽  
Victor Il’ev
Keyword(s):  
Local Search ◽  
Graph Clustering ◽  
Clustering Problem

An SVD based Real Coded Genetic Algorithm for Graph Clustering

2016 ◽  
Vol 5 (2) ◽  
pp. 64
Author(s):  
Parthajit Roy ◽  
Jyotsna Kumar Mandal
Keyword(s):  
Genetic Algorithm ◽  
Bipartite Graph ◽  
Correlation Coefficients ◽  
Graph Clustering ◽  
Similarity Matrix ◽  
Clustering Model ◽  
Real Coded Genetic Algorithm ◽  
Matrix Correlation ◽  
Test Points ◽  
Value Decomposition

This paper proposes a novel graph clustering model based on genetic algorithm using a random point bipartite graph. The model uses random points distributed uniformly in the data space and the measurement of distance from these points to the test points have been considered as proximity. Random points and test points create an adjacency matrix. To create a similarity matrix, correlation coefficients are computed from the given bipartite graph. The eigenvectors of the singular value decomposition of the weighted similarity matrix are considered and the same are passed to an elitist GA model for identifying the cluster centers. The model has been tasted with the standard datasets and the performance has been compared with existing standard algorithms.

APPROXIMATE ALGORITHMS FOR GRAPH CLUSTERING PROBLEM

2019 ◽  
pp. 64-77
Author(s):  
V. P. Il’ev ◽  
◽  
S. D. Il’eva ◽  
A. V. Morshinin ◽  
◽  
...  
Keyword(s):  
Graph Clustering ◽  
Approximate Algorithms ◽  
Clustering Problem

The graph clustering problem has a perfect zero-knowledge interactive proof

1999 ◽  
Vol 69 (4) ◽  
pp. 201-206 ◽  
Author(s):  
Alfredo De Santis ◽  
Giovanni Di Crescenzo ◽  
Oded Goldreich ◽  
Giuseppe Persiano
Keyword(s):  
Graph Clustering ◽  
Interactive Proof ◽  
Zero Knowledge ◽  
Clustering Problem

scholarly journals Market Graph Clustering via QUBO and Digital Annealing

2021 ◽  
Vol 14 (1) ◽  
pp. 34
Author(s):  
Seo Woo Hong ◽  
Pierre Miasnikof ◽  
Roy Kwon ◽  
Yuri Lawryshyn
Keyword(s):  
Graph Clustering ◽  
Small Subset ◽  
Index Tracking ◽  
Np Hard ◽  
Tracking Problem ◽  
Financial Industry ◽  
Graph Models ◽  
Clustering Problem ◽  
Market Graph ◽  
Common Task

We present a novel technique for cardinality-constrained index-tracking, a common task in the financial industry. Our approach is based on market graph models. We model our reference indices as market graphs and express the index-tracking problem as a quadratic K-medoids clustering problem. We take advantage of a purpose-built hardware architecture to circumvent the NP-hard nature of the problem and solve our formulation efficiently. The main contributions of this article are bridging three separate areas of the literature, market graph models, K-medoid clustering and quadratic binary optimization modeling, to formulate the index-tracking problem as a binary quadratic K-medoid graph-clustering problem. Our initial results show we accurately replicate the returns of various market indices, using only a small subset of their constituent assets. Moreover, our binary quadratic formulation allows us to take advantage of recent hardware advances to overcome the NP-hard nature of the problem and obtain solutions faster than with traditional architectures and solvers.

ADAPTIVE K-MEANS ALGORITHM FOR OVERLAPPED GRAPH CLUSTERING

2012 ◽  
Vol 22 (05) ◽  
pp. 1250018 ◽  
Author(s):  
GEMA BELLO-ORGAZ ◽  
HÉCTOR D. MENÉNDEZ ◽  
DAVID CAMACHO
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Graph Theory ◽  
Clustering Algorithms ◽  
Graph Clustering ◽  
Clustering Problem ◽  
Fitness Functions ◽  
Automatic Adaptation ◽  
Clustering Approach ◽  
Definition Of

The graph clustering problem has become highly relevant due to the growing interest of several research communities in social networks and their possible applications. Overlapped graph clustering algorithms try to find subsets of nodes that can belong to different clusters. In social network-based applications it is quite usual for a node of the network to belong to different groups, or communities, in the graph. Therefore, algorithms trying to discover, or analyze, the behavior of these networks needed to handle this feature, detecting and identifying the overlapped nodes. This paper shows a soft clustering approach based on a genetic algorithm where a new encoding is designed to achieve two main goals: first, the automatic adaptation of the number of communities that can be detected and second, the definition of several fitness functions that guide the searching process using some measures extracted from graph theory. Finally, our approach has been experimentally tested using the Eurovision contest dataset, a well-known social-based data network, to show how overlapped communities can be found using our method.

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