In this paper,we define the core entropy for postcritically-finite Newton maps and study its continuity within this family.We show that the entropy function is not continuous in this family,which is different from the...In this paper,we define the core entropy for postcritically-finite Newton maps and study its continuity within this family.We show that the entropy function is not continuous in this family,which is different from the polynomial case,and describe completely the continuity of the entropy function at the generic parameters.展开更多
社区搜索用于返回包含给定查询结点且符合查询条件的密集连通子图.目前,大部分已有社区搜索方法主要关注社区的结构,没有考虑到特定应用中资源受限的情况,且忽略了社区的属性特征,无法满足用户对社区搜索的个性化要求.针对该问题,本文...社区搜索用于返回包含给定查询结点且符合查询条件的密集连通子图.目前,大部分已有社区搜索方法主要关注社区的结构,没有考虑到特定应用中资源受限的情况,且忽略了社区的属性特征,无法满足用户对社区搜索的个性化要求.针对该问题,本文提出了规模受限的影响力社区搜索(Size-Constrained Influential Community search,SCIC),设计了基于深度优先搜索的基础算法,在此基础上进一步提出了基于结点预处理、剪枝规则和贪心策略的优化算法,用于减少冗余计算,加速枚举过程.在10个不同规模的数据集上进行实验,实验结果表明基础算法在搜索获得的社区规模和影响力上均优于已有算法,同时,本文提出的优化算法能够显著提升搜索效率,将响应时间缩减至基础算法的1%.展开更多
为了利用图模式挖掘犯罪情报网络中的核心团伙和核心人物,提高犯罪网络威胁预测和识别的效率,提出一种新的核心团伙挖掘算法(Core Gang Mining Algorithm,CGMA).对海量的犯罪情报网络数据集建立相应的无向简单图模型,通过改进图挖掘方式...为了利用图模式挖掘犯罪情报网络中的核心团伙和核心人物,提高犯罪网络威胁预测和识别的效率,提出一种新的核心团伙挖掘算法(Core Gang Mining Algorithm,CGMA).对海量的犯罪情报网络数据集建立相应的无向简单图模型,通过改进图挖掘方式,构建候选核心团伙集的数据结构,并提出由k-团伙通过连接和扩展2种操作得到(k+1)-团伙,从各个不同的图数据中统计其频度,最后在模拟数据集和真实数据集上验证算法CGMA的准确性和时间复杂度.该算法避免了传统的图模式挖掘中的子图同构问题,同时也优于其他常用的犯罪团伙挖掘算法.试验结果表明:该算法能对犯罪核心团伙信息进行有效预测.展开更多
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
社会网络中社团核心的发现是目前研究界和产业界关注的热点问题。现有算法把社团处理为特定约束下的图后,将社团核心发现规约为紧凑子图的提取,但对于动态约束下的多图效率很低。为此,提出基于图密度的动态约束社团核心挖掘方法——CCDC...社会网络中社团核心的发现是目前研究界和产业界关注的热点问题。现有算法把社团处理为特定约束下的图后,将社团核心发现规约为紧凑子图的提取,但对于动态约束下的多图效率很低。为此,提出基于图密度的动态约束社团核心挖掘方法——CCDCD(community core mining with dynamic constrains based on graphdensity)。主要工作包括:(1)分析约束条件变化下,关于社团的图密度变化规律;(2)提出约束变化下,社团图密度的近似求解算法DCUE(dynamic calculation based on updated edges);(3)通过实验表明,与现有方法相比,对较大规模的社团图,新方法能获得更好解,降低时间消耗80%以上;验证了动态约束能发现更多有兴趣度的知识。展开更多
The K-core of a graph is the maximal subgraph within which each vertex is connected to at least K other vertices. It is a fundamental network concept for understanding threshold cascading processes with a discontinuou...The K-core of a graph is the maximal subgraph within which each vertex is connected to at least K other vertices. It is a fundamental network concept for understanding threshold cascading processes with a discontinuous percolation transition. A minimum attack set contains the smallest number of vertices whose removal induces complete collapse of the K-core. Here we tackle this prototypical optimal initial-condition problem from the spin-glass perspective of cycle-tree maximum packing and propose a cycle-tree guided attack(CTGA) message-passing algorithm. The good performance and time efficiency of CTGA are verified on the regular random and Erd?s-Rényi random graph ensembles. Our central idea of transforming a long-range correlated dynamical process to static structural patterns may also be instructive to other hard optimization and control problems.展开更多
基金supported by National Natural Science Foundation of China (Grant Nos.11871354 and 12131016)。
文摘In this paper,we define the core entropy for postcritically-finite Newton maps and study its continuity within this family.We show that the entropy function is not continuous in this family,which is different from the polynomial case,and describe completely the continuity of the entropy function at the generic parameters.
文摘社区搜索用于返回包含给定查询结点且符合查询条件的密集连通子图.目前,大部分已有社区搜索方法主要关注社区的结构,没有考虑到特定应用中资源受限的情况,且忽略了社区的属性特征,无法满足用户对社区搜索的个性化要求.针对该问题,本文提出了规模受限的影响力社区搜索(Size-Constrained Influential Community search,SCIC),设计了基于深度优先搜索的基础算法,在此基础上进一步提出了基于结点预处理、剪枝规则和贪心策略的优化算法,用于减少冗余计算,加速枚举过程.在10个不同规模的数据集上进行实验,实验结果表明基础算法在搜索获得的社区规模和影响力上均优于已有算法,同时,本文提出的优化算法能够显著提升搜索效率,将响应时间缩减至基础算法的1%.
文摘为了利用图模式挖掘犯罪情报网络中的核心团伙和核心人物,提高犯罪网络威胁预测和识别的效率,提出一种新的核心团伙挖掘算法(Core Gang Mining Algorithm,CGMA).对海量的犯罪情报网络数据集建立相应的无向简单图模型,通过改进图挖掘方式,构建候选核心团伙集的数据结构,并提出由k-团伙通过连接和扩展2种操作得到(k+1)-团伙,从各个不同的图数据中统计其频度,最后在模拟数据集和真实数据集上验证算法CGMA的准确性和时间复杂度.该算法避免了传统的图模式挖掘中的子图同构问题,同时也优于其他常用的犯罪团伙挖掘算法.试验结果表明:该算法能对犯罪核心团伙信息进行有效预测.
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.
文摘社会网络中社团核心的发现是目前研究界和产业界关注的热点问题。现有算法把社团处理为特定约束下的图后,将社团核心发现规约为紧凑子图的提取,但对于动态约束下的多图效率很低。为此,提出基于图密度的动态约束社团核心挖掘方法——CCDCD(community core mining with dynamic constrains based on graphdensity)。主要工作包括:(1)分析约束条件变化下,关于社团的图密度变化规律;(2)提出约束变化下,社团图密度的近似求解算法DCUE(dynamic calculation based on updated edges);(3)通过实验表明,与现有方法相比,对较大规模的社团图,新方法能获得更好解,降低时间消耗80%以上;验证了动态约束能发现更多有兴趣度的知识。
基金supported by the National Natural Science Foundation of China(Grant Nos.11975295,and 12047503)and the Chinese Academy of Sciences(Grant Nos.QYZDJ-SSW-SYS018,and XDPD15)。
文摘The K-core of a graph is the maximal subgraph within which each vertex is connected to at least K other vertices. It is a fundamental network concept for understanding threshold cascading processes with a discontinuous percolation transition. A minimum attack set contains the smallest number of vertices whose removal induces complete collapse of the K-core. Here we tackle this prototypical optimal initial-condition problem from the spin-glass perspective of cycle-tree maximum packing and propose a cycle-tree guided attack(CTGA) message-passing algorithm. The good performance and time efficiency of CTGA are verified on the regular random and Erd?s-Rényi random graph ensembles. Our central idea of transforming a long-range correlated dynamical process to static structural patterns may also be instructive to other hard optimization and control problems.
