@article {13,
	title = {Identification of leaders, lurkers, associates and spammers in a social network: context-dependent and context-independent approaches},
	journal = {Social Network Analysis and Mining},
	volume = {1},
	year = {2011},
	pages = {241{\textendash}254},
	abstract = {<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>
},
	keywords = {Context dependent and context independent data analysis, Fuzzy logic, MLP, Naive Bayesian classifier, Random Forest, Social networks, Twitter},
	issn = {1869-5469},
	doi = {10.1007/s13278-011-0017-9},
	url = {http://dx.doi.org/10.1007/s13278-011-0017-9},
	author = {Fazeen, Mohamed and Ram Dantu and Guturu, Parthasarathy}
}
@article {227,
	title = {An Architecture for IP-based Next Generation Radio Access Network},
	journal = {Fourth-Generation Wireless Networks: Applications and Innovations: Applications and Innovations},
	year = {2009},
	pages = {61},
	author = {Ram Dantu and Guturu, Parthasarathy}
}
@article {26,
	title = {An Impatient Evolutionary Algorithm With Probabilistic Tabu Search for Unified Solution of Some NP-Hard Problems in Graph and Set Theory via Clique Finding},
	journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)},
	volume = {38},
	year = {2008},
	pages = {645 - 666},
	abstract = {<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>
},
	keywords = {Algorithm design and analysis, Clique finding, combinatorial complexity, Complexity theory, computational complexity, evolutionary algorithm, evolutionary algorithms (EAs), evolutionary computation, genetic algorithm, Genetic algorithms, graph theory, Heuristic algorithms, maximum clique finding, metaheuristic global optimization method, NP-hard problem, NP-hard problems in set and graph theory, optimisation, Pareto analysis, Performance analysis, probabilistic tabu search, probability, search problems, set theory, simulated annealing},
	doi = {10.1109/TSMCB.2008.915645},
	author = {Guturu, Parthasarathy and Ram Dantu}
}
