The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform
Riccardo Ortale - One of the best experts on this subject based on the ideXlab platform.
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Integrating overlapping community discovery and Role Analysis: Bayesian probabilistic generative modeling and mean-field variational inference
Engineering Applications of Artificial Intelligence, 2020Co-Authors: Gianni Costa, Riccardo OrtaleAbstract:Abstract The joint modeling of community discovery and Role Analysis was shown useful to explain, predict and reason on network topology. Nonetheless, earlier research on the integration of both tasks suffers from major limitations. Foremost, a key aspect of Role Analysis, i.e., the strength of Role-to-Role interactions, is ignored. Moreover, two fundamental properties of networks are disregarded, i.e., heterogeneity in the connectivity structure of communities and the growing link probability with node involvement in common communities. Additionally, scalability with network size is limited. In this manuscript, we incrementally develop two new machine learning approaches to deal with the foresaid issues. The proposed approaches consist in performing inference under as many Bayesian generative models of networks with overlapping communities and Roles. Under both models, nodes are associated with communities and Roles through suitable affiliations, that are dichotomized for link directionality. The strength of such affiliations is captured through nonnegative latent random variables, drawn from Gamma priors. Besides, link establishment is explained by both models through Poisson distributions. In particular, under the second model, the parameterizing rate of the Poisson distribution also accommodates the strength of Role-to-Role interactions, as captured via latent mixed-membership stochastic blockmodeling. On sparse networks, the adoption of the Poisson distribution expedites model inference. On this point, mean-field variational inference is derived and implemented as a coordinate-ascent algorithm, for the exploratory and unsupervised Analysis of node affiliations. Comparative experiments on several real-world networks demonstrate the superiority of the proposed approaches in community discovery, link prediction as well as scalability.
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Topic-aware joint Analysis of overlapping communities and Roles in social media
International Journal of Data Science and Analytics, 2019Co-Authors: Gianni Costa, Riccardo OrtaleAbstract:Topic modeling can be used to improve the mutuality and interpenetration of community discovery and Role Analysis in social media. Also, it is useful to uncover communities and Roles that are both social and topic-aware. In the present manuscript, we explore the exploitation of topic modeling to inform the seamless integration of community discovery and Role Analysis. For this purpose, we develop an innovative generative model of social media, in which the interrelation among communities, Roles and topics is explained from a fully Bayesian perspective. Essentially, communities, Roles and topics are latent factors that interact in an underlying generative process, to govern link formation and message wording. Posterior inference under the devised model allows for a variety of exploratory, descriptive and predictive tasks. These include the detection and interpretation of overlapping communities, Roles and topics as well as the prediction of missing links. We derive the mathematical details of variational inference and design a coordinate-ascent algorithm implementing the latter. An empirical assessment on real-world social media demonstrates a superior accuracy of the proposed model in community discovery and link prediction compared to several established competitors, which substantiates the rationality of both our modeling effort and the underlying intuition.
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PAKDD (2) - Marrying Community Discovery and Role Analysis in Social Media via Topic Modeling
Advances in Knowledge Discovery and Data Mining, 2018Co-Authors: Gianni Costa, Riccardo OrtaleAbstract:We explore the adoption of topic modeling to inform the seamless integration of community discovery and Role Analysis. For this purpose, we develop a new Bayesian probabilistic generative model of social media, according to which the observation of social links and textual contents is governed by novel and intuitive relationships among latent content topics, communities and Roles. Variational inference under the devised model allows for exploratory, descriptive and predictive tasks, including the detection and interpretation of overlapping communities, Roles and topics as well as the prediction of missing links. Extensive tests on real-world social media reveal a superior accuracy of the proposed model in comparison to state-of-the-art competitors, which substantiates the rationality of the motivating intuition. The experimental results are also insightfully inspected from a qualitative viewpoint.
Gianni Costa - One of the best experts on this subject based on the ideXlab platform.
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Integrating overlapping community discovery and Role Analysis: Bayesian probabilistic generative modeling and mean-field variational inference
Engineering Applications of Artificial Intelligence, 2020Co-Authors: Gianni Costa, Riccardo OrtaleAbstract:Abstract The joint modeling of community discovery and Role Analysis was shown useful to explain, predict and reason on network topology. Nonetheless, earlier research on the integration of both tasks suffers from major limitations. Foremost, a key aspect of Role Analysis, i.e., the strength of Role-to-Role interactions, is ignored. Moreover, two fundamental properties of networks are disregarded, i.e., heterogeneity in the connectivity structure of communities and the growing link probability with node involvement in common communities. Additionally, scalability with network size is limited. In this manuscript, we incrementally develop two new machine learning approaches to deal with the foresaid issues. The proposed approaches consist in performing inference under as many Bayesian generative models of networks with overlapping communities and Roles. Under both models, nodes are associated with communities and Roles through suitable affiliations, that are dichotomized for link directionality. The strength of such affiliations is captured through nonnegative latent random variables, drawn from Gamma priors. Besides, link establishment is explained by both models through Poisson distributions. In particular, under the second model, the parameterizing rate of the Poisson distribution also accommodates the strength of Role-to-Role interactions, as captured via latent mixed-membership stochastic blockmodeling. On sparse networks, the adoption of the Poisson distribution expedites model inference. On this point, mean-field variational inference is derived and implemented as a coordinate-ascent algorithm, for the exploratory and unsupervised Analysis of node affiliations. Comparative experiments on several real-world networks demonstrate the superiority of the proposed approaches in community discovery, link prediction as well as scalability.
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Topic-aware joint Analysis of overlapping communities and Roles in social media
International Journal of Data Science and Analytics, 2019Co-Authors: Gianni Costa, Riccardo OrtaleAbstract:Topic modeling can be used to improve the mutuality and interpenetration of community discovery and Role Analysis in social media. Also, it is useful to uncover communities and Roles that are both social and topic-aware. In the present manuscript, we explore the exploitation of topic modeling to inform the seamless integration of community discovery and Role Analysis. For this purpose, we develop an innovative generative model of social media, in which the interrelation among communities, Roles and topics is explained from a fully Bayesian perspective. Essentially, communities, Roles and topics are latent factors that interact in an underlying generative process, to govern link formation and message wording. Posterior inference under the devised model allows for a variety of exploratory, descriptive and predictive tasks. These include the detection and interpretation of overlapping communities, Roles and topics as well as the prediction of missing links. We derive the mathematical details of variational inference and design a coordinate-ascent algorithm implementing the latter. An empirical assessment on real-world social media demonstrates a superior accuracy of the proposed model in community discovery and link prediction compared to several established competitors, which substantiates the rationality of both our modeling effort and the underlying intuition.
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PAKDD (2) - Marrying Community Discovery and Role Analysis in Social Media via Topic Modeling
Advances in Knowledge Discovery and Data Mining, 2018Co-Authors: Gianni Costa, Riccardo OrtaleAbstract:We explore the adoption of topic modeling to inform the seamless integration of community discovery and Role Analysis. For this purpose, we develop a new Bayesian probabilistic generative model of social media, according to which the observation of social links and textual contents is governed by novel and intuitive relationships among latent content topics, communities and Roles. Variational inference under the devised model allows for exploratory, descriptive and predictive tasks, including the detection and interpretation of overlapping communities, Roles and topics as well as the prediction of missing links. Extensive tests on real-world social media reveal a superior accuracy of the proposed model in comparison to state-of-the-art competitors, which substantiates the rationality of the motivating intuition. The experimental results are also insightfully inspected from a qualitative viewpoint.
Peiyu Ren - One of the best experts on this subject based on the ideXlab platform.
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Intuitionistic fuzzy social network position and Role Analysis
Cluster Computing, 2018Co-Authors: Hua Wang, Maozhu Jin, Peiyu RenAbstract:In social network Analysis, the attribute values of actors and their relationship values are only expressed as 0 and 1. In reality, there are both cooperation and competition among actors, and their attribute values and relationship values are more complicated. The intuitionistic fuzzy number is used to construct the attribute values and the relationship values between actors in social network. The concept of position, Role and equivalence in intuitionistic fuzzy social network is redefined. The position and Role Analysis method of intuitionistic fuzzy social network is therefore proposed. Finally, a numerical case is used to demonstrate the efficiency and feasibility of the theory in this paper.
Phadrea D. Ponds - One of the best experts on this subject based on the ideXlab platform.
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Using Role Analysis to Plan for Stakeholder Involvement: A Wyoming Case Study
Wildlife Society Bulletin, 2006Co-Authors: Nina Burkardt, Phadrea D. PondsAbstract:Prior to implementing laws and policies regulating water, wildlife, wetlands, endangered species, and recreation, natural resource managers often solicit public input. Concomitantly, managers are continually seeking more effective ways to involve stakeholders. In the autumn of 1999, the Wyoming Game and Fish Department sought to develop a state management plan for its portion of the Yellowstone grizzly bear (Ursus arctos horribilis) population if it was removed from the federal threatened species list. A key aspect of developing this plan was the involvement of federal, state, and local agencies, representatives from nongovernmental organizations, and citizens. Wyoming wildlife managers asked researchers from the United States Geological Survey to demonstrate how the Legal-Institutional Analysis Model could be used to initiate this process. To address these needs, we conducted similar workshops for a group of state and federal managers or staffers and a broad group of stakeholders. Although we found similarities among the workshop groups, we also recorded differences in perspective between stakeholder groups. The managers group acknowledged the importance of varied stakeholders but viewed the grizzly bear planning process as one centered on state interests, influenced by state policies, and amenable to negotiation. The other workshops identified many stakeholders and viewed the decision process as diffuse, with many opportunities for entry into the process. These latter groups were less certain about the chance for a successful negotiation. We concluded that if these assumptions and differences were not reconciled, the public involvement effort was not likely to succeed.
Hua Wang - One of the best experts on this subject based on the ideXlab platform.
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Intuitionistic fuzzy social network position and Role Analysis
Cluster Computing, 2018Co-Authors: Hua Wang, Maozhu Jin, Peiyu RenAbstract:In social network Analysis, the attribute values of actors and their relationship values are only expressed as 0 and 1. In reality, there are both cooperation and competition among actors, and their attribute values and relationship values are more complicated. The intuitionistic fuzzy number is used to construct the attribute values and the relationship values between actors in social network. The concept of position, Role and equivalence in intuitionistic fuzzy social network is redefined. The position and Role Analysis method of intuitionistic fuzzy social network is therefore proposed. Finally, a numerical case is used to demonstrate the efficiency and feasibility of the theory in this paper.
