面向上下文图形可视化挖掘企业网络行为

在企业分布式网络系统中,精确地识别谁在做什么日益具有挑战性。目前的网络管理系统依赖于对用户身份的推理,此类方法由于收集到的数据或缩放比例粗糙,从而在大规模的网络环境下不能精确地对网络行为挖掘、发现和管理。对主机、用户、应用程序和数据访问等网络上下文内容进行可视化挖掘、发现,从而为网络管理过程的动态化提供了重要帮助。     

二元关系传递性的判定

直接用教材中的定义来判定关系的传递性,有时比较困难,而从关系传递性的等价定义、关系矩阵、关系图、关系的复合、关系的传递闭包等方面出发可得到判定其传递性的直观、简捷的方法。     

The Cauchy-Schwarz divergence and Parzen windowing: Connections to graph theory and Mercer kernels

This paper contributes a tutorial level discussion of some interesting properties of the recent Cauchy-Schwarz (CS) divergence measure between probability density functions. This measure brings together elements from several different machine learning fields, namely information theory, graph theory and Mercer kernel and spectral theory. These connections are revealed when estimating the CS divergence non-parametrically using the Parzen window technique for density estimation. An important consequence of these connections is that they enhance our understanding of the different machine learning schemes relative to each other.     

校园卡充值点最优位置的数学模型研究

选址问题目前学术界已有较多的研究成果,但大多数是将总费用作为目标函数,一般要求事先给出网络结点的位置坐标,且无需考虑结点间的最短路程,旨在确定新的地理几何中心。而对已有网络,在不改变原有路径及各结点位置的条件下,以总路程最小为目标函数,在现有网络结点中寻找其中某些结点的最优位置却是一个新的研究课题。本文以某高校校园卡充值点为例,将校园示意图转化为赋权连通图,求得该连通图的邻接矩阵,利用Floyd算法及图论软件包构造一个最短路径矩阵,得到一个赋权完全图,利用穷举法或混合整数规划法及数学软件求解,得到各学院、楼栋、学生宿舍区到三个校园卡充值点的最短总路程及三个校园卡充值点的最优位置。     

基于符号有向图的故障诊断系统及其软件平台

基于符号有向图(SDG)的故障诊断系统不依赖精确数学模型和在线数据,适用于外场通用故障诊断设备实施现场诊断。建立图形化SDG模型开发平台是实现其通用性的关键。基于Visio的软件实现通过模具设计和功能扩展降低了平台开发成本,提高了平台运行的可靠性,也使得现场维护人员可以方便地利用其扩展诊断设备功能。     

TSVFN: Two-Stage Visual Fusion Network for multimodal relation extraction

Multimodal relation extraction is a critical task in information extraction, aiming to predict the class of relations between head and tail entities from linguistic sequences and related images. However, the current works are vulnerable to less relevant visual objects detected from images and are not able to sufficiently fuse visual information into text pre-trained models. To overcome these problems, we propose a Two-Stage Visual Fusion Network (TSVFN) that employs the multimodal fusion approach in vision-enhanced entity relation extraction. In the first stage, we design multimodal graphs, whose novelty lies mainly in transforming the sequence learning into the graph learning. In the second stage, we merge the transformer-based visual representation into the text pre-trained model by a multi-scale cross-model projector. Specifically, two multimodal fusion operations are implemented inside the pre-trained model respectively. We finally accomplish deep interaction of multimodal multi-structured data in two fusion stages. Extensive experiments are conducted on a dataset (MNRE), our model outperforms the current state-of-the-art method by 1.76%, 1.52%, 1.29%, and 1.17% in terms of accuracy, precision, recall, and F1 score, respectively. Moreover, our model also achieves excellent results under the condition of fewer samples.     

Graph neural topic model with commonsense knowledge

Traditional topic models are based on the bag-of-words assumption, which states that the topic assignment of each word is independent of the others. However, this assumption ignores the relationship between words, which may hinder the quality of extracted topics. To address this issue, some recent works formulate documents as graphs based on word co-occurrence patterns. It assumes that if two words co-occur frequently, they should have the same topic. Nevertheless, it introduces noise edges into the model and thus hinders topic quality since two words co-occur frequently do not mean that they are on the same topic. In this paper, we use the commonsense relationship between words as a bridge to connect the words in each document. Compared to word co-occurrence, the commonsense relationship can explicitly imply the semantic relevance between words, which can be utilized to filter out noise edges. We use a relational graph neural network to capture the relation information in the graph. Moreover, manifold regularization is utilized to constrain the documents’ topic distributions. Experimental results on a public dataset show that our method is effective at extracting topics compared to baseline methods.     

Negative Can Be Positive: Signed Graph Neural Networks for Recommendation

Most of the existing GNN-based recommender system models focus on learning users’ personalized preferences from these (explicit/implicit) positive feedback to achieve personalized recommendations. However, in the real-world recommender system, the users’ feedback behavior also includes negative feedback behavior (e.g., click dislike button), which also reflects users’ personalized preferences. How to utilize negative feedback is a challenging research problem. In this paper, we first qualitatively and quantitatively analyze the three kinds of negative feedback that widely existed in real-world recommender systems and investigate the role of negative feedback in recommender systems. We found that it is different from what we expected — not all negative items are ranked low, and some negative items are even ranked high in the overall items. Then, we propose a novel Signed Graph Neural Network Recommendation model (SiGRec) to encode the users’ negative feedback behavior. Our SiGRec can learn positive and negative embeddings of users and items via positive and negative graph neural network encoders, respectively. Besides, we also define a new Sign Cosine (SiC) loss function to adaptively mine the information of negative feedback for different types of negative feedback. Extensive experiments on four datasets demonstrate the proposed model outperforms several existing models. Specifically, on the Zhihu dataset, SiGRec outperforms the unsigned GNN model (i.e., LightGCN), 27.58% 29.81%, and 31.21% in P@20, R@20, and nDCG@20, respectively. We hope our work can open the door to further exploring the negative feedback in recommendations.     

Phishing scams detection via temporal graph attention network in Ethereum

Recently, phishing scams have become one of the most serious types of crime involved in Ethereum, the second-largest blockchain-based cryptocurrency platform. The existing phishing scams detection techniques for Ethereum mostly use traditional machine learning or network representation learning to mine the key information from the transaction network and identify phishing addresses. However, these methods typically crop the temporal transaction graph into snapshot sequences or construct temporal random wanderings to model the dynamic evolution of the topology of the transaction graph. In this paper, we propose PDTGA, a method that applies graph representation learning based on temporal graphs attention to improve the effectiveness of phishing scams detection in Ethereum. Specifically, we learn the functional representation of time directly and model the time signal through the interactions between the time encoding function and node features, edge features, and the topology of the graph. We collected a real-world Ethereum phishing scam dataset, containing over 250,000 transaction records between more than 100,000 account addresses, and divided them into three datasets of different sizes. Through data analysis, we first summarized the periodic pattern of Ethereum phishing scam activities. Then we constructed 14 kinds of account node features and 3 kinds of transaction edge features. Experimental evaluations based on the above three datasets demonstrate that PDTGA with 94.78% AUC score and 88.76% recall score outperforms the state-of-the-art methods.