A Model Based Metaheuristic for Hybrid Hierarchical Community Structure in Social Networks

In recent years, the study of community detection in social networks has received great attention. The hierarchical structure of the network leads to the emergence of the convergence to a locally optimal community structure. In this paper, we aim to avoid this local optimum in the introduced hybrid hierarchical method. To achieve this purpose, we present an objective function where we incorporate the value of structural and semantic similarity based modularity and a metaheuristic namely bees colonies algorithm to optimize our objective function on both hierarchical level divisive and agglomerative. In order to assess the efficiency and the accuracy of the introduced hybrid bee colony model, we perform an extensive experimental evaluation on both synthetic and real networks

Constructing a robust protein-protein interaction network by integrating multiple public databases

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions (PPIs) are a critical component for many underlying biological processes. A PPI network can provide insight into the mechanisms of these processes, as well as the relationships among different proteins and toxicants that are potentially involved in the processes. There are many PPI databases publicly available, each with a specific focus. The challenge is how to effectively combine their contents to generate a robust and biologically relevant PPI network.</p> <p>Methods</p> <p>In this study, seven public PPI databases, BioGRID, DIP, HPRD, IntAct, MINT, REACTOME, and SPIKE, were used to explore a powerful approach to combine multiple PPI databases for an integrated PPI network. We developed a novel method called <it>k</it>-votes to create seven different integrated networks by using values of <it>k</it> ranging from 1-7. Functional modules were mined by using SCAN, a Structural Clustering Algorithm for Networks. Overall module qualities were evaluated for each integrated network using the following statistical and biological measures: (1) modularity, (2) similarity-based modularity, (3) clustering score, and (4) enrichment.</p> <p>Results</p> <p>Each integrated human PPI network was constructed based on the number of votes (<it>k</it>) for a particular interaction from the committee of the original seven PPI databases. The performance of functional modules obtained by SCAN from each integrated network was evaluated. The optimal value for <it>k</it> was determined by the functional module analysis. Our results demonstrate that the <it>k</it>-votes method outperforms the traditional union approach in terms of both statistical significance and biological meaning. The best network is achieved at <it>k</it>=2, which is composed of interactions that are confirmed in at least two PPI databases. In contrast, the traditional union approach yields an integrated network that consists of all interactions of seven PPI databases, which might be subject to high false positives.</p> <p>Conclusions</p> <p>We determined that the k-votes method for constructing a robust PPI network by integrating multiple public databases outperforms previously reported approaches and that a value of k=2 provides the best results. The developed strategies for combining databases show promise in the advancement of network construction and modeling.</p

Extension of graph clustering algorithms based on SCAN method in order to target weighted graphs

In this thesis we evaluate current neighbour-based graph clustering algorithms: SCAN, DHSCAN, and AHSCAN. These algorithms possess the ability to identify special nodes in graphs such as hubs and outliers. We propose and extension for each of these in order to support weighted edges. We further implemented two graph generating frameworks to create test cases. In addition we used a graph derived from the ENRON email log. We also implemented a Fast Modularity clustering algorithm, which is considered as one of the top graph clustering algorithms nowadays. One of three sets of experiments showed that results produced by extended algorithms were better than one of the reference algorithms, in other words more nodes were classified correctly. Other experiments revealed some limitations of the newly proposed methods where we noted that they do not perform as well on other types of graphs. Hence, the proposed algorithms perform best on social graphs with pronounced community structure

Clustering algorithms for anti-money laundering using graph theory and social network analysis

HEMOLIA (a project under European community's 7th framework programme) is a new generation Anti-Money Laundering (AML) intelligent multi-agent alert and investigation system which in addition to the traditional financial data makes extensive use of modern society's huge telecom data source, thereby opening up a new dimension of capabilities to all Money Laundering fighters (FIUs, LEAs) and Financial Institutes (Banks, Insurance Companies, etc.). This Master-Thesis project is done at AIA, one of the partners for the HEMOLIA project in Barcelona. The objective of this thesis is to find the clusters in a network drawn by using the financial data. An extensive literature survey has been carried out and several standard algorithms related to networks have been studied and implemented. The clustering problem is a NP-hard problem and several algorithms like K-Means and Hierarchical clustering are being implemented for studying several problems relating to sociology, evolution, anthropology etc. However, these algorithms have certain drawbacks which make them very difficult to implement. The thesis suggests (a) a possible improvement to the K-Means algorithm, (b) a novel approach to the clustering problem using the Genetic Algorithms and (c) a new algorithm for finding the cluster of a node using the Genetic Algorithm