Kirchhoff矩阵树定理的一个推广

Kirchhoff矩阵树定理是图论中的一个重要定理 ,本文对Kinchhoff矩阵树定理进行推广 ,给出图的k -支撑林的数目与其Kirchhof矩阵n -k阶子式的关系     

图的存储和访问方法

空间网络,例如道路图,是空间数据库应用中发展最快的一种.空间网络数据通常被建模为图,其结点是嵌入空间中的点.对于路径评估和最短路径计算,空间网络是通过get-a-Successor()和get-Successor()操作来访问.这些操作的高效实现通常是基于结点之闻的连通性,而不是基于结点之间的欧几里得距离.     

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.     

Criminal Action Graph: A semantic representation model of judgement documents for legal charge prediction

Semantic information in judgement documents has been an important source in Artificial Intelligence and Law. Sequential representation is the traditional structure for analyzing judgement documents and supporting the legal charge prediction task. The main problem is that it is not effective to represent the criminal semantic information. In this paper, to represent and verify the criminal semantic information such as multi-linked legal features, we propose a novel criminal semantic representation model, which constructs the Criminal Action Graph (CAG) by extracting criminal actions linked in two temporal relationships. Based on the CAG, a Graph Convolutional Network is also adopted as the predictor for legal charge prediction. We evaluate the validity of CAG on the confusing charges which composed of 32,000 judgement documents on five confusing charge sets. The CAG reaches about 88% accuracy averagely, more than 3% over the compared model. The experimental standard deviation also show the stability of our model, which is about 0.0032 on average, nearly 0. The results show the effectiveness of our model for representing and using the semantic information in judgement documents.     

Syntax-based dynamic latent graph for event relation extraction

This paper focuses on extracting temporal and parent–child relationships between news events in social news. Previous methods have proved that syntactic features are valid. However, most previous methods directly use the static outcomes parsed by syntactic parsing tools, but task-irrelevant or erroneous parses will inevitably degrade the performance of the model. In addition, many implicit higher-order connections that are directly related and critical to tasks are not explicitly exploited. In this paper, we propose a novel syntax-based dynamic latent graph model (SDLG) for this task. Specifically, we first apply a syntactic type-enhanced attention mechanism to assign different weights to different connections in the parsing results, which helps to filter out noisy connections and better fuse the information in the syntactic structures. Next, we introduce a dynamic event pair-aware induction graph to mine the task-related latent connections. It constructs a potential attention matrix to complement and correct the supervised syntactic features, using the semantics of the event pairs as a guide. Finally, the latent graph, together with the syntactic information, is fed into the graph convolutional network to obtain an improved representation of the event to complete relational reasoning. We have conducted extensive experiments on four public benchmarks, MATRES, TCR, HiEve and TB-Dense. The results show that our model outperforms the state-of-the-art model by 0.4%, 1.5%, 3.0% and 1.3% in F1 scores on the four datasets, respectively. Finally, we provide detailed analyses to show the effectiveness of each proposed component.     

检测逻辑函数对称性的新方法

本文论述了逻辑函数的Ｒｅｅｄ－Ｍｕｌｌｅｒ展开及ｂ＿ｊ系数图，在此基础上提出了直接检测基于Ｒｅｅｄ－Ｍｕｌｌｅｒ展开的逻辑函数的对称性的新方法。     

局部对称黎曼流形的伪脐点子流形

研究了局部对称黎曼流形的伪脐子流形，得到了这种子流形的一个内蕴刚性定理，从而推广了献[3]中的结果。     

一类有零迹的对称随机矩阵特征值反问题的可解性

谱σ={λ_1,λ_2,λ_3,0,….0}中至多有3个非零特征值λ_1,λ_2,λ_3,且λ_1≥0≥λ_2≥λ_3,λ_1+λ_2+λ_3=0,在某些特殊情况下,构造n×n阶对称随机矩阵使其以σ为谱的特征值反问题虽已解决,但当n是奇数时,以σ为谱的n×n阶对称随机矩阵是不存在的。     

基于专利文献和知识图谱的技术预测方法研究

在分析现有基于专利文献进行技术预测方法不足的基础上,提出一种基于专利文献和知识图谱的技术预测方法。(1)使用Google知识图谱和领域知识创建领域知识图谱;(2)依据创建的领域知识图谱对专利文献赋予标签;(3)引入社会网络社区进化研究成果,基于专利文献标签之间的网络图进行新兴技术预测。以肺癌领域技术预测为例,绘制肺癌领域知识图谱,进行方法验证并预测。验证结果显示,该方法可较好地进行技术预测。