The Experts below are selected from a list of 276 Experts worldwide ranked by ideXlab platform
Jeffrey C Grossman - One of the best experts on this subject based on the ideXlab platform.
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origins of Structural Hole traps in hydrogenated amorphous silicon
Physical Review Letters, 2013Co-Authors: Eric Johlin, Lucas K Wagner, Tonio Buonassisi, Jeffrey C GrossmanAbstract:: The inherently disordered nature of hydrogenated amorphous silicon (a-Si:H) obscures the influence of atomic features on the trapping of Holes. To address this, we have created a set of over two thousand ab initio structures of a-Si:H and explored the influence of geometric factors on the occurrence of deep Hole traps using density-functional theory. Statistical analysis of the relative contribution of various structures to the trap distribution shows that floating bonds and ionization-induced displacements correlate most strongly with Hole traps in our ensemble.
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origins of Structural Hole traps in hydrogenated amorphous silicon
APS, 2013Co-Authors: Eric Johlin, Lucas K Wagner, Tonio Buonassisi, Jeffrey C GrossmanAbstract:King Fahd University of Petroleum and Minerals (Center for Clean Water and Clean Energy at MIT and KFUPM under Project No. R1-CE-08)
Chengfei Liu - One of the best experts on this subject based on the ideXlab platform.
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Efficient Algorithms for the Identification of Top-$k$ Structural Hole Spanners in Large Social Networks
IEEE Transactions on Knowledge and Data Engineering, 2017Co-Authors: Mojtaba Rezvani, Weifa Liang, Chengfei LiuAbstract:Recent studies show that individuals in a social network can be divided into different groups of densely connected communities, and these individuals who bridge different communities, referred to as Structural Hole spanners, have great potential to acquire resources/information from communities and thus benefit from the access. Structural Hole spanners are crucial in many real applications such as community detections, diffusion controls, viral marketing, etc. In spite of their importance, little attention has been paid to them. Particularly, how to accurately characterize the Structural Hole spanners and how to devise efficient yet scalable algorithms to find them in a large social network are fundamental issues. In this paper, we study the top- $k$ Structural Hole spanner problem. We first provide a novel model to measure the quality of Structural Hole spanners through exploiting the Structural Hole spanner properties. Due to its NP-hardness, we then devise two efficient yet scalable algorithms, by developing innovative filtering techniques that can filter out unlikely solutions as quickly as possible, while the proposed techniques are built up on fast estimations of the upper and lower bounds on the cost of an optimal solution and make use of articulation points in real social networks. We finally conduct extensive experiments to validate the effectiveness of the proposed model, and to evaluate the performance of the proposed algorithms using real world datasets. The experimental results demonstrate that the proposed model can capture the characteristics of Structural Hole spanners accurately, and the Structural Hole spanners found by the proposed algorithms are much better than those by existing algorithms in all considered social networks, while the running times of the proposed algorithms are very fast.
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identifying top k Structural Hole spanners in large scale social networks
Conference on Information and Knowledge Management, 2015Co-Authors: Mojtaba Rezvani, Weifa Liang, Chengfei LiuAbstract:Recent studies have shown that in social networks, users who bridge different communities, known as Structural Hole spanners, have great potentials to acquire available resources from these communities and gain access to multiple sources of information flow. Structural Hole spanners are crucial in many applications such as community detections, diffusion controls, and viral marketing. In spite of their importance, not much attention has been paid to them. Particularly, how to characterize the Structural Hole spanner properties and how to devise efficient yet scalable algorithms to find them are fundamental issues. In this paper, we formulate the problem as the top-k Structural Hole spanner problem. Specifically, we first provide a generic model to measure the quality of Structural Hole spanners, by exploring their properties, and show that the problem is NP-hard. We then devise efficient and scalable algorithms, by exploiting the bounded inverse closeness centralities of vertices and making use of articulation points of the network. We finally evaluate the performance of the proposed algorithms through extensive experiments on real and synthetic datasets, and validate the effectiveness of the proposed model. Our experimental results demonstrate that the proposed model can capture the characteristics of Structural Hole spanners accurately, and the proposed algorithms are very promising.
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CIKM - Identifying Top- k Structural Hole Spanners in Large-Scale Social Networks
Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 2015Co-Authors: Mojtaba Rezvani, Weifa Liang, Chengfei LiuAbstract:Recent studies have shown that in social networks, users who bridge different communities, known as Structural Hole spanners, have great potentials to acquire available resources from these communities and gain access to multiple sources of information flow. Structural Hole spanners are crucial in many applications such as community detections, diffusion controls, and viral marketing. In spite of their importance, not much attention has been paid to them. Particularly, how to characterize the Structural Hole spanner properties and how to devise efficient yet scalable algorithms to find them are fundamental issues. In this paper, we formulate the problem as the top-k Structural Hole spanner problem. Specifically, we first provide a generic model to measure the quality of Structural Hole spanners, by exploring their properties, and show that the problem is NP-hard. We then devise efficient and scalable algorithms, by exploiting the bounded inverse closeness centralities of vertices and making use of articulation points of the network. We finally evaluate the performance of the proposed algorithms through extensive experiments on real and synthetic datasets, and validate the effectiveness of the proposed model. Our experimental results demonstrate that the proposed model can capture the characteristics of Structural Hole spanners accurately, and the proposed algorithms are very promising.
Mario Benassi - One of the best experts on this subject based on the ideXlab platform.
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Trapped in Your Own Net? Network Cohesion, Structural Holes, and the Adaptation of Social Capital
Organization Science, 2000Co-Authors: Maurizio Gargiulo, Mario BenassiAbstract:This paper explores the tension between two opposite views on how networks create social capital. Network closure (Coleman 1988) stresses the role of cohesive ties in fostering a normative environment that facilitates cooperation. Structural Hole theory (Burt 1992) sees cohesive ties as a source of rigidity that hinders the coordination of complex organizational tasks. The two theories lead to opposite predictions on how the structure of an actor's network may affect his ability to adapt that network to a significant change in task environment. Using data from a newly created special unit within the Italian subsidiary of a multinational computer manufacturer, we show that managers with cohesive communication networks were less likely to adapt these networks to the change in coordination requirements prompted by their new assignments, which in turn jeopardized their role as facilitators of horizontal cooperation within a newly created business unit structure. We conclude with a discussion of the trade-off between the safety of cooperation within cohesive networks and the flexibility provided by networks rich in Structural Holes.
Weifa Liang - One of the best experts on this subject based on the ideXlab platform.
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Identifying Structural Hole spanners to maximally block information propagation
Information Sciences, 2019Co-Authors: Weifa Liang, Ning Yang, Shaobing GaoAbstract:Abstract An individual can obtain high profits by playing a bridge role among different communities in a social network, thus acquiring more potential resources from the communities or having control over the information transmitted within the network. Such an individual usually is referred to as a Structural Hole spanner in the network. Existing studies on the identification of important Structural Hole spanners focused on only whether a person bridges multiple communities without emphasizing the tie strength of that person connecting to his bridged communities. However, a recent study showed that such tie strength heavily affects the profit the person obtains by playing the bridge role. In this study, we aim to identify the most important Hole spanners in a large-scale social network who have strong ties with their bridged communities. Accordingly, we first formulate the top-k Structural Hole spanner problem to identify k nodes in the network such that their removals will block the maximum numbers of information propagations within the network. Due to the NP-hardness of the problem, we then propose a novel ( 1 − ϵ ) -approximation algorithm, where ϵ is a given constant with 0
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Efficient Algorithms for the Identification of Top-$k$ Structural Hole Spanners in Large Social Networks
IEEE Transactions on Knowledge and Data Engineering, 2017Co-Authors: Mojtaba Rezvani, Weifa Liang, Chengfei LiuAbstract:Recent studies show that individuals in a social network can be divided into different groups of densely connected communities, and these individuals who bridge different communities, referred to as Structural Hole spanners, have great potential to acquire resources/information from communities and thus benefit from the access. Structural Hole spanners are crucial in many real applications such as community detections, diffusion controls, viral marketing, etc. In spite of their importance, little attention has been paid to them. Particularly, how to accurately characterize the Structural Hole spanners and how to devise efficient yet scalable algorithms to find them in a large social network are fundamental issues. In this paper, we study the top- $k$ Structural Hole spanner problem. We first provide a novel model to measure the quality of Structural Hole spanners through exploiting the Structural Hole spanner properties. Due to its NP-hardness, we then devise two efficient yet scalable algorithms, by developing innovative filtering techniques that can filter out unlikely solutions as quickly as possible, while the proposed techniques are built up on fast estimations of the upper and lower bounds on the cost of an optimal solution and make use of articulation points in real social networks. We finally conduct extensive experiments to validate the effectiveness of the proposed model, and to evaluate the performance of the proposed algorithms using real world datasets. The experimental results demonstrate that the proposed model can capture the characteristics of Structural Hole spanners accurately, and the Structural Hole spanners found by the proposed algorithms are much better than those by existing algorithms in all considered social networks, while the running times of the proposed algorithms are very fast.
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identifying top k Structural Hole spanners in large scale social networks
Conference on Information and Knowledge Management, 2015Co-Authors: Mojtaba Rezvani, Weifa Liang, Chengfei LiuAbstract:Recent studies have shown that in social networks, users who bridge different communities, known as Structural Hole spanners, have great potentials to acquire available resources from these communities and gain access to multiple sources of information flow. Structural Hole spanners are crucial in many applications such as community detections, diffusion controls, and viral marketing. In spite of their importance, not much attention has been paid to them. Particularly, how to characterize the Structural Hole spanner properties and how to devise efficient yet scalable algorithms to find them are fundamental issues. In this paper, we formulate the problem as the top-k Structural Hole spanner problem. Specifically, we first provide a generic model to measure the quality of Structural Hole spanners, by exploring their properties, and show that the problem is NP-hard. We then devise efficient and scalable algorithms, by exploiting the bounded inverse closeness centralities of vertices and making use of articulation points of the network. We finally evaluate the performance of the proposed algorithms through extensive experiments on real and synthetic datasets, and validate the effectiveness of the proposed model. Our experimental results demonstrate that the proposed model can capture the characteristics of Structural Hole spanners accurately, and the proposed algorithms are very promising.
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CIKM - Identifying Top- k Structural Hole Spanners in Large-Scale Social Networks
Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 2015Co-Authors: Mojtaba Rezvani, Weifa Liang, Chengfei LiuAbstract:Recent studies have shown that in social networks, users who bridge different communities, known as Structural Hole spanners, have great potentials to acquire available resources from these communities and gain access to multiple sources of information flow. Structural Hole spanners are crucial in many applications such as community detections, diffusion controls, and viral marketing. In spite of their importance, not much attention has been paid to them. Particularly, how to characterize the Structural Hole spanner properties and how to devise efficient yet scalable algorithms to find them are fundamental issues. In this paper, we formulate the problem as the top-k Structural Hole spanner problem. Specifically, we first provide a generic model to measure the quality of Structural Hole spanners, by exploring their properties, and show that the problem is NP-hard. We then devise efficient and scalable algorithms, by exploiting the bounded inverse closeness centralities of vertices and making use of articulation points of the network. We finally evaluate the performance of the proposed algorithms through extensive experiments on real and synthetic datasets, and validate the effectiveness of the proposed model. Our experimental results demonstrate that the proposed model can capture the characteristics of Structural Hole spanners accurately, and the proposed algorithms are very promising.
Qiaoyu Zhou - One of the best experts on this subject based on the ideXlab platform.
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Structural Hole-based approach to control public opinion in a social network
Engineering Applications of Artificial Intelligence, 2020Co-Authors: Cheng Gong, Xiaoliang Chen, Yakun Wang, Qiaoyu ZhouAbstract:Abstract Structural Hole spanners play an important role in information diffusion. Compared with opinion leaders, Structural Hole spanners have better locations in social networks to expand the scope of information diffusion. In the past, researchers focused on evolution rules and opinion dynamics environments to monitor and even manage public opinion. In this study, we propose a novel Structural-Hole-based approach to control public opinion in social networks, hereinafter referred to as the SHCPO approach. We discuss the influence of both ordinary agents and Structural Hole spanners on opinion evolution using our improved Friedkin–Johnsen (FJ) model. Further, we analyze the evolution tendency of public opinion, which leads to the final consensus of public opinion, via the FJ model with ordinary agents in a community and Structural Hole spanners in joint communities. We reveal three kinds of connections between Structural Hole spanners and ordinary agents in joint communities. These comprise Structural Hole spanners connecting (1) two opinion leaders; (2) two ordinary agents; (3) one opinion leader and one ordinary agent. The three connections will lead to different opinion evolution conditions. According to the Structural balance theory, we reconstruct the social network by changing the connections between Structural Hole spanners and agents in different communities. This guides the public opinion tendencies of joint communities towards the positive. Experimental results demonstrate beneficial effects of the SHCPO approach. We use three evaluation indicators to compare the SHCPO approach to five alternative methods. The percentage of positive opinions is used as an evaluation indicator. The SHCPO approach, compared with adding informed agents, add edges, the method from WWW and varying susceptibility to persuasion method, which guide the agent with a negative opinion towards positive opinion, has improved about 17%, 10%, 9%, 1%, respectively.
