02573nas a2200241   4500008004100000022001400041245013800055210006900193300001400262490000600276520177600282653006002058653001602118653000802134653003002142653001802172653002002190653001202210100002002222700001502242700002602257856004802283       2011                        eng d  a1869-546900aIdentification of leaders, lurkers, associates and spammers in a social network: context-dependent and context-independent approaches0 aIdentification of leaders lurkers associates and spammers in a s  a241–2540 v13 a<p>In this paper, we present two methods for classification of different social network actors (individuals or organizations) such as leaders (e.g., news groups), lurkers, spammers and close associates. The first method is a two-stage process with a fuzzy-set theoretic (FST) approach to evaluation of the strengths of network links (or equivalently, actor-actor relationships) followed by a simple linear classifier to separate the actor classes. Since this method uses a lot of contextual information including actor profiles, actor-actor tweet and reply frequencies, it may be termed as a context-dependent approach. To handle the situation of limited availability of actor data for learning network link strengths, we also present a second method that performs actor classification by matching their short-term (say, roughly 25&nbsp;days) tweet patterns with the generic tweet patterns of the prototype actors of different classes. Since little contextual information is used here, this can be called a context-independent approach. Our experimentation with over 500 randomly sampled records from a twitter database consists of 441,234 actors, 2,045,804 links, 6,481,900 tweets, and 2,312,927 total reply messages indicates that, in the context-independent analysis, a multilayer perceptron outperforms on both on classification accuracy and a new F-measure for classification performance, the Bayes classifier and Random Forest classifiers. However, as expected, the context-dependent analysis using link strengths evaluated using the FST approach in conjunction with some actor information reveals strong clustering of actor data based on their types, and hence can be considered as a superior approach when data available for training the system is abundant.</p>
10aContext dependent and context independent data analysis10aFuzzy logic10aMLP10aNaive Bayesian classifier10aRandom Forest10aSocial networks10aTwitter1 aFazeen, Mohamed1 aDantu, Ram1 aGuturu, Parthasarathy  uhttp://dx.doi.org/10.1007/s13278-011-0017-900372nas a2200109   4500008004100000245007000041210006600111300000700177100001500184700002600199856003700225       2009                        eng d00aAn Architecture for IP-based Next Generation Radio Access Network0 aArchitecture for IPbased Next Generation Radio Access Network  a611 aDantu, Ram1 aGuturu, Parthasarathy  uhttps://nsl.cse.unt.edu/node/12203364nas a2200421   4500008004100000245016000041210006900201300001400270490000700284520195900291653003402250653001902284653002902303653002202332653002902354653002702383653003402410653002902444653002202473653002302495653001702518653002502535653002702560653004502587653002002632653004502652653001702697653002002714653002502734653003002759653001602789653002002805653001502825653002402840100002602864700001502890856003702905       2008                        eng d00aAn Impatient Evolutionary Algorithm With Probabilistic Tabu Search for Unified Solution of Some NP-Hard Problems in Graph and Set Theory via Clique Finding0 aImpatient Evolutionary Algorithm With Probabilistic Tabu Search   a645 - 6660 v383 a<p>Many graph- and set-theoretic problems, because of their tremendous application potential and theoretical appeal, have been well investigated by the researchers in complexity theory and were found to be NP-hard. Since the combinatorial complexity of these problems does not permit exhaustive searches for optimal solutions, only near-optimal solutions can be explored using either various problem-specific heuristic strategies or metaheuristic global-optimization methods, such as simulated annealing, genetic algorithms, etc. In this paper, we propose a unified evolutionary algorithm (EA) to the problems of maximum clique finding, maximum independent set, minimum vertex cover, subgraph and double subgraph isomorphism, set packing, set partitioning, and set cover. In the proposed approach, we first map these problems onto the maximum clique-finding problem (MCP), which is later solved using an evolutionary strategy. The proposed impatient EA with probabilistic tabu search (IEA-PTS) for the MCP integrates the best features of earlier successful approaches with a number of new heuristics that we developed to yield a performance that advances the state of the art in EAs for the exploration of the maximum cliques in a graph. Results of experimentation with the 37 DIMACS benchmark graphs and comparative analyses with six state-of-the-art algorithms, including two from the smaller EA community and four from the larger metaheuristics community, indicate that the IEA-PTS outperforms the EAs with respect to a Pareto-lexicographic ranking criterion and offers competitive performance on some graph instances when individually compared to the other heuristic algorithms. It has also successfully set a new benchmark on one graph instance. On another benchmark suite called Benchmarks with Hidden Optimal Solutions, IEA-PTS ranks second, after a very recent algorithm called COVER, among its peers that have experimented with this suite.</p>
10aAlgorithm design and analysis10aClique finding10acombinatorial complexity10aComplexity theory10acomputational complexity10aevolutionary algorithm10aevolutionary algorithms (EAs)10aevolutionary computation10agenetic algorithm10aGenetic algorithms10agraph theory10aHeuristic algorithms10amaximum clique finding10ametaheuristic global optimization method10aNP-hard problem10aNP-hard problems in set and graph theory10aoptimisation10aPareto analysis10aPerformance analysis10aprobabilistic tabu search10aprobability10asearch problems10aset theory10asimulated annealing1 aGuturu, Parthasarathy1 aDantu, Ram  uhttps://nsl.cse.unt.edu/node/207