Ranking Related Research Papers Based on Citation Graph

Research papers are of great importance for researchers to record and share their ideas, studies and results. Meanwhile, they are the bases for further research work. Finding related research papers is the preliminary of good research. A new method for ranking related research papers is introduced in this paper. Given a set of interested papers, the method proposed in this paper may rank related papers just based on a citation graph by constructing the minimal Steiner trees. The method can work with the existing keyword based methods. Intensive experiments on a specific research topic show that the proposed method provides a different ranking criterion to existing ones, and can effectively retrieve those highly related papers.     

图的符号控制划分数的Nordhaus-Gaddum型结果

设G=(V,E)是一个简单图,在图G的所有符号(全)控制族中,基数最大的符号(全)控制族包含的符号(全)控制函数的数目称为是图G的符号(全)控制划分数.首先给出图的符号控制划分数的Nordhaus-Gaddum型结果,接下来,又给出了图的符号全控制划分数的Nordhaus-Gaddum型结果.     

49阶Steiner三连系的构造与计数

阐明了v=t^2阶Steiner三连系构造的基本思路。给出了任意完全图kv的边矩阵的定义，从而为图论研究提供了一个工具。提出了t^2阶Steiner三连系构造的一种方法。介绍了49阶Steiner三连系构造的全过程。讨论了t^2阶Steiner三连系的计数问题。     

几种图类的邻点可区别关联着色

近年来,关于图着色问题的研究得到了许多有价值的结果,同时拓展出若干新的着色.图的邻点可区别关联着色是在图的关联着色概念的基础上提出的一种新的着色概念.本文研究了路、星、扇、轮、完全图的邻点可区别关联着色并确定了它们的邻点可区别关联色数.     

求解频率指配问题改进的完全图算法

以四级预测模型为基础,提出改进的完全图算法,并推导出该算法计算量的解析式,从理论上证明了改进的完全图算法性能上的提高.四级预测模型来判断两电台之间的兼容性,而改进的完全图算法调用四级预测模型来搜索出多电台间的所有可用频组,进行了小规模电台数目电子信息系统的完整电磁兼容分析,完成了系统内的频率指配.实际工程应用的结果与理论一致.     

均衡二分图中存在哈密顿因子的条件

研究了在均衡二分图中包含给定哈密顿圈的[k,k+1]因子的存在性问题.根据图论中因子和临界图理论,并结合代数知识,针对均衡二分图,研究图的阶数,顶点的度和因子之间的关系,分情况讨论,通过对均衡二分图的临界条件的限制,给出均衡二分图中存在包含给定的哈密顿圈的[k,k+1]因子的充分条件.该条件在很大程度上改进了已有的包含哈密顿圈的度条件,进一步完善了包含哈密顿圈的因子理论.算例表明所用方法的有效性,所得结论的正确性.     

LDPC码概述及若干理论和应用问题

低密度奇偶校验码（LDPC码）被普遍认为是当今最先进的一种纠错码（信道编码）。LDPC码，图模型，迭代译码的研究热潮已在全球流行有10年以上。这篇论文高度概括的简述了LDPC码的背景和基本知识，从一些独特的视角并联系多个领域提出一些对LDPC码相关知识的理解方法和见解主张，总结了LDPC码当今最新的研究进展。从理论和应用两方面提出作者认为非常有意义的研究问题。     

具有给定稳定数和连通性的极值图(Ⅱ)

讨论了一些相关问题：（1）已知连通度特征化极（非哈密尔顿）图；（2）特征化已知独立数极（非哈密尔顿）图；（3）特征化极（非哈密尔顿）图；（4）特征化极BC－闭图。     

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).     

具有较小最大度的临界图边数的下界

运用Discharge法给出了不含2点和3点的6临界图及不含4点和5点的7临界图边数的下界．