Search Result Clustering via Randomized Partitioning of Query-Induced Subgraphs

In this paper, we present an approach to search result clustering, using partitioning of underlying link graph. We define the notion of "query-induced subgraph" and formulate the problem of search result clustering as a problem of efficient partitioning of given subgraph into topic-related clusters. Also, we propose a novel algorithm for approximative partitioning of such graph, which results in cluster quality comparable to the one obtained by deterministic algorithms, while operating in more efficient computation time, suitable for practical implementations. Finally, we present a practical clustering search engine developed as a part of this research and use it to get results about real-world performance of proposed concepts.Comment: 16th Telecommunications Forum TELFOR 200

Network Community Detection on Metric Space

Community detection in a complex network is an important problem of much interest in recent years. In general, a community detection algorithm chooses an objective function and captures the communities of the network by optimizing the objective function, and then, one uses various heuristics to solve the optimization problem to extract the interesting communities for the user. In this article, we demonstrate the procedure to transform a graph into points of a metric space and develop the methods of community detection with the help of a metric defined for a pair of points. We have also studied and analyzed the community structure of the network therein. The results obtained with our approach are very competitive with most of the well-known algorithms in the literature, and this is justified over the large collection of datasets. On the other hand, it can be observed that time taken by our algorithm is quite less compared to other methods and justifies the theoretical findings

Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale

Notions of community quality underlie network clustering. While studies surrounding network clustering are increasingly common, a precise understanding of the realtionship between different cluster quality metrics is unknown. In this paper, we examine the relationship between stand-alone cluster quality metrics and information recovery metrics through a rigorous analysis of four widely-used network clustering algorithms -- Louvain, Infomap, label propagation, and smart local moving. We consider the stand-alone quality metrics of modularity, conductance, and coverage, and we consider the information recovery metrics of adjusted Rand score, normalized mutual information, and a variant of normalized mutual information used in previous work. Our study includes both synthetic graphs and empirical data sets of sizes varying from 1,000 to 1,000,000 nodes. We find significant differences among the results of the different cluster quality metrics. For example, clustering algorithms can return a value of 0.4 out of 1 on modularity but score 0 out of 1 on information recovery. We find conductance, though imperfect, to be the stand-alone quality metric that best indicates performance on information recovery metrics. Our study shows that the variant of normalized mutual information used in previous work cannot be assumed to differ only slightly from traditional normalized mutual information. Smart local moving is the best performing algorithm in our study, but discrepancies between cluster evaluation metrics prevent us from declaring it absolutely superior. Louvain performed better than Infomap in nearly all the tests in our study, contradicting the results of previous work in which Infomap was superior to Louvain. We find that although label propagation performs poorly when clusters are less clearly defined, it scales efficiently and accurately to large graphs with well-defined clusters

A simple and fast exact clustering algorithm defined for complex networks and based on the properties of primes

In this paper a new clustering method based on primes is proposed. This method define a nodes cluster of any complex network, considering the nodes with same input/output number and same number of paths with equal length, so all the network nodes with analogous functions will be possible to identify. The clustering algorithm proposed, results very efficient because it is defined on simple computations with primes. For example, with our algorithm the analysis of a network with 500 nodes and 124750 connections is performed in 80 seconds on Pentium 4 with CPU 2Ghz and 1Gb ram. Keywords: Complex network, clustering method, graph theory, unidirectional/bidirectional network, complete path