强边着色猜想问题的最优图

著名图论专家Erds和Nesetǐil对图的强边色数上界提出了一个猜想:当最大度Δ为偶数时,χ's(G)≤5/4Δ~2;当最大度Δ为奇数时,χ's(G)≤1/4(5Δ~2-2Δ+1);并且给出了当Δ=4时的最优图.此处构造了一族图,并证明了当最大度为奇数时,如果Erd9s和Ne2etǐil提出的强边着色猜想成立,则猜想中的上界是最优的.     

Leibniz代数的导子扩张

通过给出强双导子的概念,证明强双导子可以给出Leibniz代数的导子扩张,并给出构造Leibniz代数的一种新方法.     

一类三圈图关于Merrifield-Simmons指标的排序

研究了一类三圈图瓦的Merrifield-Simmons指标，根据Cq上三种不同的连接方式，给出了该类三圈图关于Merrifield-Simmons指标的排序．     

关于星匹配数的图能量下界

对图G的能量ε(G)与K_(1,s)-匹配数μ_s(G)之间的关系进行了研究。证明了对于一般图G有■成立,进一步地,若其子图满足一定的条件,则有■,其中c_1(G)表示G中的奇圈数。还证明了若n阶树T的最大度小于等于3,有ε(T)≥(s+1)μ_s(T)-1成立。     

基于图注意力网络的案件罪名预测方法:CP-GAT

案件罪名预测任务是基于文本数据去预测案件所属罪名.针对现有方法在相似罪名和长尾数据集上表现不佳的问题，提出了一种基于图注意力网络的案件罪名预测方法CP-GAT(charge prediction based on graph attention network).该方法首先使用司法文书数据集中的案例事件描述文本和案例对应的法条信息建立异质图结构数据，构建后的异质图包含两种类型的节点(词节点、案例节点)，两种类型的边(词节点与词节点相连的边，词节点与案例节点相连的边).在基于法律文本构建后的异质图上使用图注意力网络进行图特征提取，最后将得到的特征向量输入到罪名预测的分类器中，得到案例所属的罪名.在CAIL2018法律数据集上的实验结果表明，基于图注意力网络的罪名预测方法优于对比实验使用的方法，准确率和宏观F1值分别达到了95.2%和66.1，验证了提出的方法有利于提升案件罪名预测任务的性能.     

An Overview of Kernelization Algorithms for Graph Modification Problems

Kernelization algorithms for graph modification problems are important ingredients in parameterized computation theory. In this paper, we survey the kernelization algorithms for four types of graph modification problems, which include vertex deletion problems, edge editing problems, edge deletion problems, and edge completion problems. For each type of problem, we outline typical examples together with recent results, analyze the main techniques, and provide some suggestions for future research in this field.     

基于primal RankRLS方法的本体映射算法

本体映射作为实现多本体间相互操作的重要手段,已广泛应用于诸多领域.应用原始RankRLS算法,将多本体图映射到实数轴,由此将本体图中每个顶点都映射成实数.通过比较两概念对应实数间的差值得到本体映射.实验表明,该算法有较高的效率.     

非连通图3C8m∪C8m-1∪G的优美标号

研究非连通图3C8m∪C8m-1∪G的优美性.证明如下结论：对任意正整数m,若图G是特征为k且缺标号值k＋24m-2的交错图,则非连通图3C8m∪C8m-1∪G存在缺标号值k＋1的优美标号.     

高校图书馆在新疆和谐社会建设中的作用研究

论述了图书馆在构建新疆和谐社会建设中的地位和作用,探讨了加强图书馆建设、发挥其教育职能的途径。     

Latent Co-interests' Relationship Prediction

With the development of the social media and Internet, discovering latent information from massive information is becoming particularly relevant to improving user experience. Research efforts based on preferences and relationships between users have attracted more and more attention. Predictive problems, such as inferring friend relationship and co-author relationship between users have been explored. However, many such methods are based on analyzing either node features or the network structures separately, few have tried to tackle both of them at the same time. In this paper, in order to discover latent co-interests’ relationship, we not only consider users’ attributes but network information as well. In addition, we propose an Interest-based Factor Graph Model (I-FGM) to incorporate these factors. Experiments on two data sets (bookmarking and music network) demonstrate that this predictive method can achieve better results than the other three methods (ANN, NB, and SVM).