基于DFS的图的双向连通性研究

提出了一个基于DFS的图双向连通性研究的简单算法,本算法具有容易理解、形式规范的特点,无论用于教学还是解决实际问题,都有较大的实用价值。     

基于C++ Builder的不规则图形动画的实现

本文以一个具体实例，介绍了如何在C＋＋Builder中，对不规则图形进行处理，以及如何利用双缓冲原理，以实现不规则图形的逼真动画技术.说明了其原理，并给出部分程序的源代码。     

Towards automated software model checking using graph transformation systems and Bogor

Graph transformation systems have become a general formal modeling language to describe many models in software development process. Behavioral modeling of dynamic systems and model-to-model transformations are only a few examples in which graphs have been used to software development. But even the perfect graph transformation system must be equipped with automated analysis capabilities to let users understand whether such a formal specification fulfills their requirements. In this paper, we present a new solution to verify graph transformation systems using the Bogor model checker. The attributed graph grammars （AGG）-like graph transformation systems are translated to Bandera intermediate representation （BIR）, the input language of Bogor, and Bogor verifies the model against some interesting properties defined by combining linear temporal logic （LTL） and special-purpose graph rules. Experimental results are encouraging, showing that in most cases our solution improves existing approaches in terms of both performance and expressiveness.     

旗传递(v,k,3)-对称设计的自同构群与它们的抛物子群

文章研究了(v,k,3)-对称设计D的分类;证明了如果群G是D的几乎单型的自同构群,即存在非交换单群X使得X≤G≤Aut(D),那么X∩Ga不可能是X的抛物子群.     

Jointly modeling intra- and inter-session dependencies with graph neural networks for session-based recommendations

Recently, graph neural networks (GNNs) have achieved promising results in session-based recommendation. Existing methods typically construct a local session graph and a global session graph to explore complex item transition patterns. However, studies have seldom investigated the repeat consumption phenomenon in a local graph. In addition, it is challenging to retrieve relevant adjacent nodes from the whole training set owing to computational complexity and space constraints. In this study, we use a GNN to jointly model intra- and inter-session item dependencies for session-based recommendations. We construct a repeat-aware local session graph to encode the intra-item dependencies and generate the session representation with positional awareness. Then, we use sessions from the current mini-batch instead of the whole training set to construct a global graph, which we refer to as the session-level global graph. Next, we aggregate the K-nearest neighbors to generate the final session representation, which enables easy and efficient neighbor searching. Extensive experiments on three real-world recommendation datasets demonstrate that RN-GNN outperforms state-of-the-art methods.     

中国专利奖视角下的颠覆性技术识别分析

为充分利用专利结构化数据和文本数据,实现准确有效的颠覆性技术识别,以中国专利奖为切入点,提出基于图神经网络的颠覆性技术识别框架。首先以获得中国专利奖的授权发明专利定义颠覆性技术,解决技术定义难的问题;接着使用Neo4j图数据库构建异质有向图,存储专利多重关系数据和方向信息,解决关系数据利用率低的问题;最后使用关系图卷积神经网络（R-GCN）模型进行训练,实现颠覆性技术识别,解决识别效果不佳的问题。研究表明以获得中国专利奖的专利技术直接定义颠覆性技术是合理且可靠的;提出的颠覆性技术识别框架能充分利用专利数据信息和专利异质有向图中空间信息,识别出绝大部分的颠覆性技术,丰富了图神经网络在颠覆性技术识别方面的研究。