Directed Graph Clustering Algorithms, Topology, and Weak Links

Functional modules can be predicted using genome-wide protein–protein interactions (PPIs) from a systematic perspective. Various graph clustering algorithms have been applied to PPI networks for this task. In particular, the detection of overlapping clusters is necessary because a protein is involved in multiple functions under different conditions. graph entropy (GE) is a novel metric to assess the quality of clusters in a large, complex network. In this study, the unweighted and weighted GE algorithm is evaluated to prove the validity of predicting function modules. To measure clustering accuracy, the clustering results are compared to protein complexes and Gene Ontology (GO) annotations as references. We demonstrate that the GE algorithm is more accurate in overlapping clusters than the other competitive methods. Moreover, we confirm the biological feasibility of the proteins that occur most frequently in the set of identified clusters. Finally, novel proteins for the additional annotation of GO terms are revealed.

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Metric-Constrained Optimization for Graph Clustering Algorithms

SIAM Journal on Mathematics of Data Science ◽

10.1137/18m1217152 ◽

2019 ◽

Vol 1 (2) ◽

pp. 333-355 ◽

Cited By ~ 1

Author(s):

Nate Veldt ◽

David F. Gleich ◽

Anthony Wirth ◽

James Saunderson

Keyword(s):

Constrained Optimization ◽

Clustering Algorithms ◽

Graph Clustering

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Novel Algorithms for Graph Clustering Applied to Human Activities

Mathematics ◽

10.3390/math9101089 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1089

Author(s):

Nebojsa Budimirovic ◽

Nebojsa Bacanin

Keyword(s):

Human Activities ◽

Clustering Algorithms ◽

Weighted Graph ◽

Graph Clustering ◽

Recall Rate ◽

Number Of Clusters ◽

Starting Point ◽

Measurement Units ◽

Novel Algorithms ◽

Precision Rate

In this paper, a novel algorithm (IBC1) for graph clustering with no prior assumption of the number of clusters is introduced. Furthermore, an additional algorithm (IBC2) for graph clustering when the number of clusters is given beforehand is presented. Additionally, a new measure of evaluation of clustering results is given—the accuracy of formed clusters (T). For the purpose of clustering human activities, the procedure of forming string sequences are presented. String symbols are gained by modeling spatiotemporal signals obtained from inertial measurement units. String sequences provided a starting point for forming the complete weighted graph. Using this graph, the proposed algorithms, as well as other well-known clustering algorithms, are tested. The best results are obtained using novel IBC2 algorithm: T = 96.43%, Rand Index (RI) 0.966, precision rate (P) 0.918, recall rate (R) 0.929 and balanced F-measure (F) 0.923.

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Weight-Discounted Symmetrization in Clustering Directed Graphs

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.2979 ◽

2013 ◽

Vol 756-759 ◽

pp. 2979-2987 ◽

Cited By ~ 1

Author(s):

Bin He ◽

Hui Liu ◽

Xiang Hui Zhao ◽

Ze Feng Li

Keyword(s):

Directed Graph ◽

Real World ◽

State Of The Art ◽

Directed Graphs ◽

Clustering Algorithms ◽

Similarity Measures ◽

Citation Networks ◽

Second Stage ◽

Node Ranking ◽

Link Similarity

An increasing attention has been recently devoted to uncovering community structure in directed graphs which widely exist in real-world complex networks such as social networks, citation networks, World Wide Web, email networks, etc. A two-stage framework for detecting clusters is an effective way for clustering directed graphs while the first stage is to symmetrize the directed graph using some similarity measures. Any state-of-the-art clustering algorithms for undirected graphs can be leveraged in the second stage. Hence, both stages are important to the effectiveness of the clustering result. However, existing symmetrization methods only consider about the direction of edges but ignore the weights of nodes. In this paper, we first attempt to connect link analysis in directed graph clustering. This connection not only takes into consideration the directionality of edges but also uses node ranking scores such as authority and hub score to explicitly capture in-link and out-link similarity. We also demonstrate the generality of our proposed method by showing that existing state-of-the-art symmetrization methods can be derived from our method. Empirical validation shows that our method can find communities effectively in real world networks.

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ADAPTIVE K-MEANS ALGORITHM FOR OVERLAPPED GRAPH CLUSTERING

International Journal of Neural Systems ◽

10.1142/s0129065712500189 ◽

2012 ◽

Vol 22 (05) ◽

pp. 1250018 ◽

Cited By ~ 36

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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