The prevalence of graph data has brought a lot of attention to cohesive and dense subgraph mining.In contrast with the large number of indexes proposed to help mine dense subgraphs in general graphs,only very few inde...The prevalence of graph data has brought a lot of attention to cohesive and dense subgraph mining.In contrast with the large number of indexes proposed to help mine dense subgraphs in general graphs,only very few indexes are proposed for the same in bipartite graphs.In this work,we present the index called˛.ˇ/-core number on vertices,which reflects the maximal cohesive and dense subgraph a vertex can be in,to help enumerate the(α,β)-cores,a commonly used dense structure in bipartite graphs.To address the problem of extremely high time and space cost for enumerating the(α,β)-cores,we first present a linear time and space algorithm for computing the˛.ˇ/-core numbers of vertices.We further propose core maintenance algorithms,to update the core numbers of vertices when a graph changes by avoiding recalculations.Experimental results on different real-world and synthetic datasets demonstrate the effectiveness and efficiency of our algorithms.展开更多
Objectives: We combine Hobfoll’s Conservation of Resources (COR) theory and key components of self-help group “step work” ideology to investigate how dynamic changes in key intra-individual resource loss and gains ...Objectives: We combine Hobfoll’s Conservation of Resources (COR) theory and key components of self-help group “step work” ideology to investigate how dynamic changes in key intra-individual resource loss and gains (self-esteem, abstinence self-efficacy, existential growth) influence relapse rates in a sample of individuals in the Maintenance Stage of substance abuse recovery. Methods: Participants (n = 579) completed two surveys over a nine month period that assessed baseline and changes in intra-individual loss and gain resources as well as relapse rates over study course. Multiple regression analyses were performed to predict whether baseline and dynamic changes in intra-individual scores predict relapse rates over time. Results: Individuals that reported lower levels of resource gain at baseline, as well as decreased gain trajectories and increased loss trajectories over time were more likely to relapse. Conclusions: Findings support self-help group “step work” models and the application of COR theory for relapse likelihood prediction in a sample of individuals in longer term substance abuse recovery. Research efforts should examine the complex relationship between these dynamic intra-individual resources, social cognition, self-regulation and relapse risk. Future interventions should address the importance of the continual development and protection of these valuable intra-individual resources to prevent relapse.展开更多
基金This work was supported by the National Key Research and Development Program of China(No.2019YFB2102600)the National Natural Science Foundation of China(Nos.62122042 and 61971269)the Blockchain Core Technology Strategic Research Program of Ministry of Education of China(No.2020KJ010301)fund。
文摘The prevalence of graph data has brought a lot of attention to cohesive and dense subgraph mining.In contrast with the large number of indexes proposed to help mine dense subgraphs in general graphs,only very few indexes are proposed for the same in bipartite graphs.In this work,we present the index called˛.ˇ/-core number on vertices,which reflects the maximal cohesive and dense subgraph a vertex can be in,to help enumerate the(α,β)-cores,a commonly used dense structure in bipartite graphs.To address the problem of extremely high time and space cost for enumerating the(α,β)-cores,we first present a linear time and space algorithm for computing the˛.ˇ/-core numbers of vertices.We further propose core maintenance algorithms,to update the core numbers of vertices when a graph changes by avoiding recalculations.Experimental results on different real-world and synthetic datasets demonstrate the effectiveness and efficiency of our algorithms.
文摘Objectives: We combine Hobfoll’s Conservation of Resources (COR) theory and key components of self-help group “step work” ideology to investigate how dynamic changes in key intra-individual resource loss and gains (self-esteem, abstinence self-efficacy, existential growth) influence relapse rates in a sample of individuals in the Maintenance Stage of substance abuse recovery. Methods: Participants (n = 579) completed two surveys over a nine month period that assessed baseline and changes in intra-individual loss and gain resources as well as relapse rates over study course. Multiple regression analyses were performed to predict whether baseline and dynamic changes in intra-individual scores predict relapse rates over time. Results: Individuals that reported lower levels of resource gain at baseline, as well as decreased gain trajectories and increased loss trajectories over time were more likely to relapse. Conclusions: Findings support self-help group “step work” models and the application of COR theory for relapse likelihood prediction in a sample of individuals in longer term substance abuse recovery. Research efforts should examine the complex relationship between these dynamic intra-individual resources, social cognition, self-regulation and relapse risk. Future interventions should address the importance of the continual development and protection of these valuable intra-individual resources to prevent relapse.
