We developed a computational framework to identify common gene association sub-network. This framework combines graphical lasso model, graph product and a replicator equation based clique solver. We applied this metho...We developed a computational framework to identify common gene association sub-network. This framework combines graphical lasso model, graph product and a replicator equation based clique solver. We applied this method to find common stress responsive sub-networks from two related Deinococcus-Thermus bacterial species.展开更多
This paper proposes an energy-efficient geocast algorithm for wireless sensor networks with guaranteed de-livery of packets from the sink to all nodes located in several geocast regions. Our approach is different from...This paper proposes an energy-efficient geocast algorithm for wireless sensor networks with guaranteed de-livery of packets from the sink to all nodes located in several geocast regions. Our approach is different from those existing in the literature. We first propose a hybrid clustering scheme: in the first phase we partition the network in cliques using an existing energy-efficient clustering protocol. Next the set of clusterheads of cliques are in their turn partitioned using an energy-efficient hierarchical clustering. Our approach to con-sume less energy falls into the category of energy-efficient clustering algorithm in which the clusterhead is located in the central area of the cluster. Since each cluster is a clique, each sensor is at one hop to the cluster head. This contributes to use less energy for transmission to and from the clusterhead, comparatively to multi hop clustering. Moreover we use the strategy of asleep-awake to minimize energy consumption during extra clique broadcasts.展开更多
The search engines are indispensable tools to find information amidst massive web pages and documents. A good search engine needs to retrieve information not only in a shorter time, but also relevant to the users’ qu...The search engines are indispensable tools to find information amidst massive web pages and documents. A good search engine needs to retrieve information not only in a shorter time, but also relevant to the users’ queries. Most search engines provide short time retrieval to user queries;however, they provide a little guarantee of precision even to the highly detailed users’ queries. In such cases, documents clustering centered on the subject and contents might improve search results. This paper presents a novel method of document clustering, which uses semantic clique. First, we extracted the Features from the documents. Later, the associations between frequently co-occurring terms were defined, which were called as semantic cliques. Each connected component in the semantic clique represented a theme. The documents clustered based on the theme, for which we designed an aggregation algorithm. We evaluated the aggregation algorithm effectiveness using four kinds of datasets. The result showed that the semantic clique based document clustering algorithm performed significantly better than traditional clustering algorithms such as Principal Direction Divisive Partitioning (PDDP), k-means, Auto-Class, and Hierarchical Clustering (HAC). We found that the Semantic Clique Aggregation is a potential model to represent association rules in text and could be immensely useful for automatic document clustering.展开更多
Coauthorship networks consist of links among groups of mutually connected authors that form a clique. Classical approaches using Social Network Analysis indices do not account for this characteristic. We propose two n...Coauthorship networks consist of links among groups of mutually connected authors that form a clique. Classical approaches using Social Network Analysis indices do not account for this characteristic. We propose two new cohesion indices based on a clique approach, and we redefine the network density using an index of variance of density. We have applied these indices to two coauthorship networks, one comprising researchers that published in Mathematics Education journals and the other comprising researchers from a Computational Modeling Graduate Program. A contextualized and comparative analysis was performed to show the applicability and potential of the indices for analyzing social networks data.展开更多
Given an undirected graph,the Maximum Clique Problem(MCP)is to find a largest complete subgraph of the graph.MCP is NP-hard and has found many practical applications.In this paper,we propose a parallel Branch-and-Boun...Given an undirected graph,the Maximum Clique Problem(MCP)is to find a largest complete subgraph of the graph.MCP is NP-hard and has found many practical applications.In this paper,we propose a parallel Branch-and-Bound(BnB)algorithm to tackle this NP-hard problem,which carries out multiple bounded searches in parallel.Each search has its upper bound and shares a lower bound with the rest of the searches.The potential benefit of the proposed approach is that an active search terminates as soon as the best lower bound found so far reaches or exceeds its upper bound.We describe the implementation of our highly scalable and efficient parallel MCP algorithm,called PBS,which is based on a state-of-the-art sequential MCP algorithm.The proposed algorithm PBS is evaluated on hard DIMACS and BHOSLIB instances.The results show that PBS achieves a near-linear speedup on most DIMACS instances and a superlinear speedup on most BHOSLIB instances.Finally,we give a detailed analysis that explains the good speedups achieved for the tested instances.展开更多
文摘We developed a computational framework to identify common gene association sub-network. This framework combines graphical lasso model, graph product and a replicator equation based clique solver. We applied this method to find common stress responsive sub-networks from two related Deinococcus-Thermus bacterial species.
文摘This paper proposes an energy-efficient geocast algorithm for wireless sensor networks with guaranteed de-livery of packets from the sink to all nodes located in several geocast regions. Our approach is different from those existing in the literature. We first propose a hybrid clustering scheme: in the first phase we partition the network in cliques using an existing energy-efficient clustering protocol. Next the set of clusterheads of cliques are in their turn partitioned using an energy-efficient hierarchical clustering. Our approach to con-sume less energy falls into the category of energy-efficient clustering algorithm in which the clusterhead is located in the central area of the cluster. Since each cluster is a clique, each sensor is at one hop to the cluster head. This contributes to use less energy for transmission to and from the clusterhead, comparatively to multi hop clustering. Moreover we use the strategy of asleep-awake to minimize energy consumption during extra clique broadcasts.
文摘The search engines are indispensable tools to find information amidst massive web pages and documents. A good search engine needs to retrieve information not only in a shorter time, but also relevant to the users’ queries. Most search engines provide short time retrieval to user queries;however, they provide a little guarantee of precision even to the highly detailed users’ queries. In such cases, documents clustering centered on the subject and contents might improve search results. This paper presents a novel method of document clustering, which uses semantic clique. First, we extracted the Features from the documents. Later, the associations between frequently co-occurring terms were defined, which were called as semantic cliques. Each connected component in the semantic clique represented a theme. The documents clustered based on the theme, for which we designed an aggregation algorithm. We evaluated the aggregation algorithm effectiveness using four kinds of datasets. The result showed that the semantic clique based document clustering algorithm performed significantly better than traditional clustering algorithms such as Principal Direction Divisive Partitioning (PDDP), k-means, Auto-Class, and Hierarchical Clustering (HAC). We found that the Semantic Clique Aggregation is a potential model to represent association rules in text and could be immensely useful for automatic document clustering.
文摘Coauthorship networks consist of links among groups of mutually connected authors that form a clique. Classical approaches using Social Network Analysis indices do not account for this characteristic. We propose two new cohesion indices based on a clique approach, and we redefine the network density using an index of variance of density. We have applied these indices to two coauthorship networks, one comprising researchers that published in Mathematics Education journals and the other comprising researchers from a Computational Modeling Graduate Program. A contextualized and comparative analysis was performed to show the applicability and potential of the indices for analyzing social networks data.
基金supported by the National Natural Science Foundation of China under Grant No.62162066the Open Funding of Engineering Research Center of Cyberspace of Ministry of Education of China under Grant No.WLKJAQ202011010+1 种基金the Education Department Funding of Yunnan Province of China under Grant No.2021J0006the Spanish AEI project PID2019-111544GB-C2.
文摘Given an undirected graph,the Maximum Clique Problem(MCP)is to find a largest complete subgraph of the graph.MCP is NP-hard and has found many practical applications.In this paper,we propose a parallel Branch-and-Bound(BnB)algorithm to tackle this NP-hard problem,which carries out multiple bounded searches in parallel.Each search has its upper bound and shares a lower bound with the rest of the searches.The potential benefit of the proposed approach is that an active search terminates as soon as the best lower bound found so far reaches or exceeds its upper bound.We describe the implementation of our highly scalable and efficient parallel MCP algorithm,called PBS,which is based on a state-of-the-art sequential MCP algorithm.The proposed algorithm PBS is evaluated on hard DIMACS and BHOSLIB instances.The results show that PBS achieves a near-linear speedup on most DIMACS instances and a superlinear speedup on most BHOSLIB instances.Finally,we give a detailed analysis that explains the good speedups achieved for the tested instances.
