A Similarity Based Agglomerative Clustering Algorithm in Networks

机译：网络中基于相似度的聚集聚类算法

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The detection of clusters is benefit for understanding the organizations and functions of networks. Clusters, or communities, are usually groups of nodes densely interconnected but sparsely linked with any other clusters. To identify communities, an efficient and effective community agglomerative algorithm based on node similarity is proposed. The proposed method initially calculates similarities between each pair of nodes, and form pre-partitions according to the principle that each node is in the same community as its most similar neighbor. After that, check each partition whether it satisfies community criterion. For the pre-partitions who do not satisfy, incorporate them with others that having the biggest attraction until there are no changes. To measure the attraction ability of a partition, we propose an attraction index that based on the linked node's importance in networks. Therefore, our proposed method can better exploit the nodes' properties and network's structure. To test the performance of our algorithm, both synthetic and empirical networks ranging in different scales are tested. Simulation results show that the proposed algorithm can obtain superior clustering results compared with six other widely used community detection algorithms.

机译：群集的检测对于了解网络的组织和功能很有帮助。集群或社区通常是密集互连的节点组，但与任何其他集群稀疏地链接在一起。为了识别社区，提出了一种基于节点相似度的高效社区聚结算法。所提出的方法最初计算每对节点之间的相似度，并根据每个节点与其最相似的邻居处于同一社区的原则形成预分区。之后，检查每个分区是否满足社区标准。对于不满意的分区，请将其与其他具有最大吸引力的分区合并，直到没有变化为止。为了衡量分区的吸引力，我们提出了一个吸引力指数，该指数基于链接节点在网络中的重要性。因此，我们提出的方法可以更好地利用节点的属性和网络的结构。为了测试我们算法的性能，测试了不同规模的综合和经验网络。仿真结果表明，与其他六种广泛使用的社区检测算法相比，该算法可以获得更好的聚类结果。

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《International conference on graphic and image processing》|2017年|106155I.1-106155I.11|共11页
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Zhiyuan Liu; Xiujuan Wang; Yinghong Ma;
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cluster detection; node similarity; attraction index; community incorporation;

机译：集群检测;节点相似度吸引力指数社区成立;

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1. SDN‐Based Hierarchical Agglomerative Clustering Algorithm for Interference Mitigation in Ultra‐Dense Small Cell Networks [J] . Guang Yang, Yewen Cao, Amir Esmailpour, ETRI journal . 2018,第2期

机译：基于 SDN 的分层聚集聚类算法，用于缓解超密集小蜂窝网络中的干扰
2. AN ENERGY EFFICIENT ALGORITHM FOR WIRELESS SENSOR NETWORKS BASED ON HIERARCHICAL AGGLOMERATIVE CLUSTERING [J] . Taleb Tariq, Mejdi Kaddour Far East Journal of Electronics and Communications . 2016,第4期

机译：基于分层聚集聚类的无线传感器网络节能高效算法
3. Hierarchical Agglomerative Clustering Algorithm Based Real-Time Event Detection from Online Social Media Network [J] . AMJAD JUMAAH FRHAN WSEAS Transactions on Signal Processing . 2017,第Pta2期

机译：基于分层凝聚聚类算法从在线社交媒体网络的实时事件检测
4. A Similarity Based Agglomerative Clustering Algorithm in Networks [C] . Zhiyuan Liu, Xiujuan Wang, Yinghong Ma International Conference on Graphic and Image Processing . 2017

机译：网络中的一种相似性的凝聚聚类算法
5. FAC-PIN: An efficient and fast agglomerative clustering algorithm for protein interaction networks to predict protein complexes and functional modules. [D] . Rahman, Mohammad Shamsur. 2013

机译：FAC-PIN：一种高效且快速的聚集聚类算法，用于蛋白质相互作用网络来预测蛋白质复合物和功能模块。
6. CASS: A distributed network clustering algorithm based on structure similarity for large-scale network [O] . Jungrim Kim, Mincheol Shin, Jeongwoo Kim, 2012

机译：CASS：一种基于结构相似性的大规模网络分布式网络聚类算法
7. Figure 4: (A) One conserved sequence, which occurs 79 times in 46,264 binding site peaks from the ChIP-seq data-set. The mutation profile of this conserved sequence is illustrated, where ’_ ’ indicates this base is unchanged; DEL indicates this base is lost; INS X indicates a new base X is inserted in front of this base. (B) Several repeated elements patterns are listed. (C) In the first column, the top five DNA motifs, mined by meme-chip tools (Machanick Bailey, 2011) are illustrated. The resemblant conserved sequences, found by the CFSP algorithm are listed in the second column. In the third column, the position-specific scoring matrices, which are transformed from mutational information are listed. The similarity between meme motif and resemblant conserved sequence with PSSM format was calculated via a stamp motif comparison tool (Mahony Benos, 2007). The E-values for the similarity of those pairs is displayed in the fourth column. (D) One motif is selected in each group clustered by gkmsvm descriptors, and the corresponding motif found by the CFSP algorithm is listed below. (E) There are additional datasets (File No: ENCFF100GRL, ENCFF616IRT, ENCFF870CER, Target: SREBF1) collected from https://www.encodeproject.org. The top two motifs are selected in each file using meme tools, and the corresponding motifs found by our algorithm are listed below. [O] . -1

机译：图4：（a）一种保守序列，其发生在芯片-SEQ数据集中的46,264个结合位点峰值中的79倍。说明了这种保守序列的突变分布，其中'_'表示该碱度不变; del表示此基础丢失; INS X表示新的基础X插入此基础前面。（b）列出了几种重复的元素模式。（c）在第一栏中，示出了由MEME芯片工具（Machanick＆Bailey，2011）开采的前五个DNA主题。由CFSP算法发现的相应保守序列列于第二列中。在第三列中，列出了从突变信息转换的特定位置的评分矩阵。 MEME主题与PSSM格式的相似性与PSSM格式之间的相似性通过邮票图章比较工具（Mahony＆Benos，2007）计算。这些对相似性的电子值显示在第四列中。（d）在由GKMSVM描述符聚集的每个组中选择了一个图案，下面列出了CFSP算法的相应主题。（e）从https://www.encodeproject.org收集的，有附加数据集（文件no：cernff100grl，cenf616irl，conf8.20cer，target：srebf1）。使用MEME工具在每个文件中选择前两个图案，并且我们的算法发现的相应主题如下所示。
8. Clustering-Based Algorithms for Radial Basis Function and Sigmoid Perceptron Networks. [R] . Ulykan, Z. 2001

机译：基于聚类的径向基函数和sigmoid感知器网络算法。

A Similarity Based Agglomerative Clustering Algorithm in Networks

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