Improving Functional Modularity in Protein-Protein Interactions Graphs Using Hub-Induced Subgraphs

Ucar, Duygu; Asur, Sitaram; Çatalyürek, Ümit V.; Parthasarathy, S.

doi:10.1007/11871637_36

Cited by 43 publications

(32 citation statements)

References 20 publications

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“…An example of this approach is the algorithm proposed in [21]: hub proteins (with degree greater than a given threshold) are first selected and their neighborhood graphs are subsequently constructed. A hub-duplication strategy is then applied to detect dense subgraphs in these neighborhood graphs with multi-functional hub proteins assigned to multiple clusters.…”

Section: Related Workmentioning

confidence: 99%

DEEN: A Simple and Fast Algorithm for Network Community Detection

Jancura

Mavroeidis

Marchiori

2012

Lecture Notes in Computer Science

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Abstract. This paper introduces an algorithm for network community detection called DEEN (Delete Edges and Expand Nodes) consisting of two simple steps. First edges of the graph estimated to connect different clusters are detected and removed, next the resulting graph is used for generating communities by expanding seed nodes. DEEN uses as parameters the minimum and maximum allowed size of a cluster, and a resolution parameter whose value influences the number of removed edges. Application of DEEN to the budding yeast protein network for detecting functional protein complexes indicates its capability to identify clusters containing proteins with the same functional category, improving on MCL, a popular state-of-the-art method for functional protein complex detection. Moreover, application of DEEN to two popular benchmark networks results in the detection of accurate communities, substantiating the effectiveness of the proposed method in diverse domains.

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Section: Related Workmentioning

confidence: 99%

DEEN: A Simple and Fast Algorithm for Network Community Detection

Jancura

Mavroeidis

Marchiori

2012

Lecture Notes in Computer Science

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“…Techniques for identifying groups or communities within a network can be classified into two different categories: (i) graph partitioning based approaches [5,15,16], and (ii) modularity scoring based approaches [1,3,4,6,22,30,34]. Graph partitioning based methods generally partition different nodes into groups that share common features or topologies.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Several approaches [11,14,18,19,25,34] have been introduced to detect the multiple membership of nodes within different communities; some of these methods, which explore the underlying structure of these networks, are based on fuzzy sets [2,12,33] . Within the context of protein-protein interaction (PPI) networks, a modularity measure was developed by determining hub-induced subgraphs [30]. In a tangible approach, a method was developed to distinguish between dense and sparse subgraphs in weighted networks to identify community structure [9].…”

Section: Background and Motivationmentioning

confidence: 99%

Detection of Communities and Bridges in Weighted Networks

Saha

Domeniconi

Rangwala

2011

Machine Learning and Data Mining in Pattern Recognition

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Abstract. Traditional graph-based clustering methods group vertices into non-intersecting clusters under the assumption that each vertex can belong to only a single cluster. On the other hand, recent research on graph-based clustering methods, applied to real world networks (e.g., protein-protein interaction networks and social networks), shows overlapping patterns among the underlying clusters. For example, in social networks, an individual is expected to belong to multiple clusters (or communities), rather than strictly confining himself/herself to just one. As such, overlapping clusters enable better models of real-life phenomena. Soft clustering (e.g., fuzzy c-means) has been used with success for network data as well as non-graph data, when the objects are allowed to belong to multiple clusters with a certain degree of membership. In this paper, we propose a fuzzy clustering based approach for community detection in a weighted graphical representation of social and biological networks, for which the ground truth associated to the nodes is available. We compare our results with a baseline method for both multi-labeled and single-labeled datasets.

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“…They include hierarchical clustering, partitioning graphs to maximize quality functions such as network modularity, k-clique percolation, and some other interesting algorithmic methods [1,2]. Nevertheless, the Newman and Girvan conceptual system is often chosen as a pretext for scientific contributions due to its structural articulation and its ability to be used in a wide range of practical situations [3]. The Newman-Girvan algorithm is particularly used to compute betweenness for edges (biological links) that connect the nodes (proteins) in a network.…”

Section: Essential Previous Workmentioning

confidence: 99%

The Flag-based Algorithm - A Novel Greedy Method that Optimizes Protein Communities Detection

Bocu

Tabirca

2011

INT J COMPUT COMMUN

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Proteins and the networks they determine, called interactome networks, have received attention at an important degree during the last years, because they have been discovered to have an influence on some complex biological phenomena, such as problematic disorders like cancer. This paper presents a contribution that aims to optimize the detection of protein communities through a greedy algorithm that is implemented in the C programming language. The optimization involves a double improvement in relation to protein communities detection, which is accomplished both at the algorithmic and programming level. The resulting implementation's performance was carefully tested on real biological data and the results acknowledge the relevant speedup that the optimization determines. Moreover, the results are in line with the previous findings that our current research produced, as it reveals and confirms the existence of some important properties of those proteins that participate in the carcinogenesis process. Apart from being particularly useful for research purposes, the novel community detection algorithm also dramatically speeds up the proteomic databases analysis process, as compared to some other sequential community detection approaches, and also to the sequential algorithm of Newman and Girvan.

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Improving Functional Modularity in Protein-Protein Interactions Graphs Using Hub-Induced Subgraphs

Cited by 43 publications

References 20 publications

DEEN: A Simple and Fast Algorithm for Network Community Detection

DEEN: A Simple and Fast Algorithm for Network Community Detection

Detection of Communities and Bridges in Weighted Networks

The Flag-based Algorithm - A Novel Greedy Method that Optimizes Protein Communities Detection

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