Graph Mining Research Articles

Detecting Hawala Network for Money Laundering by Graph Mining

Detecting Hawala Network for Money Laundering by Graph Mining

Identifying shader sub-patterns for GPU performance tuning and architecture design

GPUs are increasingly playing vital roles in the modern technology industry. Improving the GPU performance involves optimizing its architectural design and fine-tuning its software code. However, to achieve this, engineers must investigate codes from as many GPU-related applications as possible to identify code portions that need fine-tuning. Moreover, this effort requires engineers to have good domain knowledge, and their work is made more arduous because the source codes of applications are normally confidential. To this end, we introduce ShaderAnalyzer, a solution leveraging graph mining and machine learning to analyze GPU-executed low-level machine codes and identify their fine-tuning opportunities. Our approach includes representing machine code with graph structure and subsequently identifying frequently occurring substructures within the codes. Optimizing the execution of these substructures can enhance the overall performance of the GPU. In addition, our model leverages these frequent patterns to further facilitate engineers’ tasks by selecting representative patterns to predict and investigate low-efficiency ones. We conduct comprehensive experiments to evaluate the performance of our solution, and the results have been validated by our industry partners. ShaderAnalyzer is an end-to-end framework that helps engineers identify code segments with the highest potential for performance gains after fine-tuning and offers valuable insights for hardware architects in future products design.

Dynamic frequent subgraph mining algorithms over evolving graphs: a survey

Frequent subgraph mining (FSM) is an essential and challenging graph mining task used in several applications of the modern data science. Some of the FSM algorithms have the objective of finding all frequent subgraphs whereas some of the algorithms focus on discovering frequent subgraphs approximately. On the other hand, modern applications employ evolving graphs where the increments are small graphs or stream of nodes and edges. In such cases, FSM task becomes more challenging due to growing data size and complexity of the base algorithms. Recently we see frequent subgraph mining algorithms designed for dynamic graph data. However, there is no comparative review of the dynamic subgraph mining algorithms focusing on the discovery of frequent subgraphs over evolving graph data. This article focuses on the characteristics of dynamic frequent subgraph mining algorithms over evolving graphs. We first introduce and compare dynamic frequent subgraph mining algorithms; trying to highlight their attributes as increment type, graph type, graph representation, internal data structure, algorithmic approach, programming approach, base algorithm and output type. Secondly, we introduce and compare the approximate frequent subgraph mining algorithms for dynamic graphs with additional attributes as their sampling strategy, data in the sample, statistical guarantees on the sample and their main objective. Finally, we highlight research opportunities in this specific domain from our perspective. Overall, we aim to introduce the research area of frequent subgraph mining over evolving graphs with the hope that this can serve as a reference and inspiration for the researchers of the field.

Sinkhorn Distance Minimization for Adaptive Semi-Supervised Social Network Alignment.

Social network alignment, aiming at linking identical identities across different social platforms, is a fundamental task in social graph mining. Most existing approaches are supervised models and require a large number of manually labeled data, which are infeasible in practice considering the yawning gap between social platforms. Recently, isomorphism across social networks is incorporated as complementary to link identities from the distribution level, which contributes to alleviating the dependency on sample-level annotations. Adversarial learning is adopted to learn a shared projection function by minimizing the distance between two social distributions. However, the hypothesis of isomorphism might not always hold true as social user behaviors are generally unpredictable, and thus a shared projection function is insufficient to handle the sophisticated cross-platform correlations. In addition, adversarial learning suffers from training instability and uncertainty, which may hinder model performance. In this article, we propose a novel meta-learning-based social network alignment model Meta-SNA to effectively capture the isomorphism and the unique characteristics of each identity. Our motivation lies in learning a shared meta-model to preserve the global cross-platform knowledge and an adaptor to learn a specific projection function for each identity. Sinkhorn distance is further introduced as the distribution closeness measurement to tackle the limitations of adversarial learning, which owns an explicitly optimal solution and can be efficiently computed by the matrix scaling algorithm. Empirically, we evaluate the proposed model over multiple datasets, and the experimental results demonstrate the superiority of Meta-SNA.

GABoost: Graph Alignment Boosting via Local Optimum Escape

Heterogeneous graphs provide a universal data structure for representing various kinds of structured data in numerous domains. The graph alignment problem aims to find the correspondences of vertices in different graphs, playing a fundamental role in many downstream tasks of heterogeneous graph mining. In recent years, many graph alignment methods have been proposed, ranging from classical optimization methods , spectral methods , to embedding learning based-methods . Due to the problem's complexity, the result found by most existing methods is either a heuristic solution or a critical point in the solution space. In this paper, we propose GABoost, a graph alignment boosting algorithm that takes as input an initial alignment between two heterogeneous graphs and outputs a boosted alignment via an iterative local-optimum-escape process. One of the distinctive features of GABoost is that it can be sequentially composed with any graph alignment methods to improve the output of upstream methods. To examine the effectiveness of GABoost, we select 7 upstream methods of graph alignment as well as 6 real-world datasets, and quantitatively investigate the degree to which GABoost boosts these methods. The results show that GABoost improves the alignment accuracy of the 7 upstream methods by 25.25% on average with acceptable time overhead.

A Knowledge Graph-Based Consistency Detection Method for Network Security Policies

Network security policy is regarded as a guideline for the use and management of the network environment, which usually formulates various requirements in the form of natural language. It can help network managers conduct standardized network attack detection and situation awareness analysis in the overall time and space environment of network security. However, in most cases, due to configuration updates or policy conflicts, there are often differences between the real network environment and network security policies. In this case, the consistency detection of network security policies is necessary. The previous consistency detection methods of security policies have some problems. Firstly, the detection direction is single, only focusing on formal reasoning methods to achieve logical consistency detection and solve problems. Secondly, the detection policy field is not comprehensive, focusing only on a certain type of problem in a certain field. Thirdly, there are numerous forms of data structures used for consistency detection, and it is difficult to unify the structured processing and analysis of rule library carriers and target information carriers. With the development of intelligent graph and data mining technology, the above problems have the possibility of optimization. This article proposes a new consistency detection approach for network security policy, which uses an intelligent graph database as a visual information carrier, which can widely connect detection information and achieve comprehensive detection across knowledge domains, physical devices, and detection methods. At the same time, it can also help users grasp the security associations with the real network environment based on the graph algorithm of the knowledge graph and intelligent reasoning. Furthermore, these actual network situations and knowledge bases can help managers improve policies more tailored to local conditions. This article also introduces the consistency detection process of typical cases of network security policies, demonstrating the practical details and effectiveness of this method.

Adaptive denoising graph contrastive learning with memory graph attention for recommendation

Graph contrastive learning has emerged as a powerful technique for dealing with graph noise and mining latent information in networks, that has been widely applied in GNN-based collaborative filtering. Traditional graph contrastive learning methods commonly generate multiple augmented views, and then learn node representations by maximizing the consistency between these views. However, on one hand, manual view construction methods necessitate expert knowledge and a trial-and-error process. On the other hand, adaptive view construction methods require decoders which results in increased training costs. To address the aforementioned limitations, in this paper, we propose the Adaptive Denoising Graph Contrastive Learning with Memory Graph Attention for Recommendation (ADGA) framework. Firstly, we introduce the memory graph attention mechanism to capture node attention during multi-hop information aggregation. Then, unlike previous methods that required additional node representations to generate views, ADGA proposes, for the first time, directly using attention to adaptively generate structure-aware contrastive learning views. It reduces the training cost of the model and improves the cross-view consistency of node representations, that offers a new paradigm for adaptive graph contrastive learning. Experimental results on three real-world datasets demonstrate that ADGA achieves state-of-the-art performance in recommendation tasks. The code is available at https://github.com/Andrewsama/ADGA.

A versatile framework for attributed network clustering via K-nearest neighbor augmentation

AbstractAttributed networks containing entity-specific information in node attributes are ubiquitous in modeling social networks, e-commerce, bioinformatics, etc. Their inherent network topology ranges from simple graphs to hypergraphs with high-order interactions and multiplex graphs with separate layers. An important graph mining task is node clustering, aiming to partition the nodes of an attributed network into k disjoint clusters such that intra-cluster nodes are closely connected and share similar attributes, while inter-cluster nodes are far apart and dissimilar. It is highly challenging to capture multi-hop connections via nodes or attributes for effective clustering on multiple types of attributed networks. In this paper, we first present as an efficient approach to attributed hypergraph clustering (AHC). includes a carefully-crafted K-nearest neighbor augmentation strategy for the optimized exploitation of attribute information on hypergraphs, a joint hypergraph random walk model to devise an effective AHC objective, and an efficient solver with speedup techniques for the objective optimization. The proposed techniques are extensible to various types of attributed networks, and thus, we develop as a versatile attributed network clustering framework, capable of attributed graph clustering, attributed multiplex graph clustering, and AHC. Moreover, we devise with algorithmic designs tailored for GPU acceleration to boost efficiency. We have conducted extensive experiments to compare our methods with 19 competitors on 8 attributed hypergraphs, 16 competitors on 6 attributed graphs, and 16 competitors on 3 attributed multiplex graphs, all demonstrating the superb clustering quality and efficiency of our methods.

Multitype view of knowledge contrastive learning for recommendation

Graph Neural Networks (GNNs) are playing an increasingly vital role in the field of recommender systems. To improve knowledge perception within GNNs, contrastive learning has been applied and has proven to be highly effective. GNNs have the ability to aggregate diverse knowledge and integrate topologies, while contrastive learning seeks supervisory signals from the model data. The combination of GNNs and contrastive learning can improve recommendations. However, thoughtless or incomplete contrastive learning settings limit the effectiveness of GNNs-based recommender systems in learning knowledge from knowledge and interaction graphs. To better exploit the valuable information within knowledge graphs, we propose a novel multitype view of knowledge contrastive learning for recommendations (MVKC) model.The MVKC model generates hierarchical views and augmented views in two modules, performing cross-hierarchical-view and cross-augmented-view contrastive learning and mining graph features in a self-supervised manner. The hierarchical views consist of global and local parts at multiple levels, while the augmented views are fused from the augmented knowledge graph and augmented interaction graph in our augmented processing. These features allow the MVKC model to alleviate the sparsity of user–item interaction graphs, suppress knowledge graph noise, and filter long-tail entities, which has been proven extremely important for a recommendation. The MVKC model also has strong anti-interference ability and robustness, which is crucial for a well-established model. Our experiments with three public datasets demonstrate that the MVKC model outperforms current state-of-the-art methods.

Fast Multiview Clustering by Optimal Graph Mining.

Multiview clustering (MVC) aims to exploit heterogeneous information from different sources and was extensively investigated in the past decade. However, far less attention has been paid to handling large-scale multiview data. In this brief, we fill this gap and propose a fast multiview clustering by an optimal graph mining model to handle large-scale data. We mine a consistent clustering structure from landmark-based graphs of different views, from which the optimal graph based on the one-hot encoding of cluster labels is recovered. Our model is parameter-free, so intractable hyperparameter tuning is avoided. An efficient algorithm of linear complexity to the number of samples is developed to solve the optimization problems. Extensive experiments on real-world datasets of various scales demonstrate the superiority of our proposal.

Introducing new node prediction in graph mining: Predicting all links from isolated nodes with Graph Neural Networks

This paper introduces new node prediction, a fresh problem in the field of graph mining and social network analysis. This task can be categorized as zero-shot out-of-graph all-links prediction, and aims to predict all links coming from or going to a new, isolated, and unobserved node that was previously disconnected from the graph. In comparison with classic approaches to link prediction (including few-shot out-of-graph link prediction), this problem bears two key differences: (1) the new node has no existing links from which to extract patterns for new predictions; and (2) the goal is to predict not just one, but all the links of this new node, or, at least, a significant part of them. Experiments used an architecture based on Deep Graph Neural Networks, and were carried out on two datasets: a bibliographic citation graph and a drug–drug interaction graph. Results demonstrate that this challenging problem can be solved satisfactorily by using state-of-the-art Deep Learning techniques.

A knowledge graph algorithm enabled deep recommendation system.

Personalized learning resource recommendations may help resolve the difficulties of online education that include learning mazes and information overload. However, existing personalized learning resource recommendation algorithms have shortcomings such as low accuracy and low efficiency. This study proposes a deep recommendation system algorithm based on a knowledge graph (D-KGR) that includes four data processing units. These units are the recommendation unit (RS unit), the knowledge graph feature representation unit (KGE unit), the cross compression unit (CC unit), and the feature extraction unit (FE unit). This model integrates technologies including the knowledge graph, deep learning, neural network, and data mining. It introduces cross compression in the feature learning process of the knowledge graph and predicts user attributes. Multimodal technology is used to optimize the process of project attribute processing; text type attributes, multivalued type attributes, and other type attributes are processed separately to reconstruct the knowledge graph. A convolutional neural network algorithm is introduced in the reconstruction process to optimize the data feature qualities. Experimental analysis was conducted from two aspects of algorithm efficiency and accuracy, and the particle swarm optimization, neural network, and knowledge graph algorithms were compared. Several tests showed that the deep recommendation system algorithm had obvious advantages when the number of learning resources and users exceeded 1,000. It has the ability to integrate systems such as the particle swarm optimization iterative classification, neural network intelligent simulation, and low resource consumption. It can quickly process massive amounts of information data, reduce algorithm complexity and requires less time and had lower costs. Our algorithm also has better efficiency and accuracy.

Jaccard-constrained dense subgraph discovery

Finding dense subgraphs is a core problem in graph mining with many applications in diverse domains. At the same time many real-world networks vary over time, that is, the dataset can be represented as a sequence of graph snapshots. Hence, it is natural to consider the question of finding dense subgraphs in a temporal network that are allowed to vary over time to a certain degree. In this paper, we search for dense subgraphs that have large pairwise Jaccard similarity coefficients. More formally, given a set of graph snapshots and input parameter α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document}, we find a collection of dense subgraphs, with pairwise Jaccard index at least α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document}, such that the sum of densities of the induced subgraphs is maximized. We prove that this problem is NP-hard and we present a greedy, iterative algorithm which runs in Onk2+m\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {O}} \\mathopen {} \\left( nk^2 + m\\right)$$\\end{document} time per single iteration, where k is the length of the graph sequence and n and m denote number of vertices and total number of edges respectively. We also consider an alternative problem where subgraphs with large pairwise Jaccard indices are rewarded. We do this by incorporating the indices directly into the objective function. More formally, given a set of graph snapshots and a weight λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda$$\\end{document}, we find a collection of dense subgraphs such that the sum of densities of the induced subgraphs plus the sum of Jaccard indices, weighted by λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda$$\\end{document}, is maximized. We prove that this problem is NP-hard. To discover dense subgraphs with good objective value, we present an iterative algorithm which runs in On2k2+mlogn+k3n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {O}} \\mathopen {}\\left( n^2k^2 + m \\log n + k^3 n\\right)$$\\end{document} time per single iteration, and a greedy algorithm which runs in On2k2+mlogn+k3n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {O}} \\mathopen {}\\left( n^2k^2 + m \\log n + k^3 n\\right)$$\\end{document} time. We show experimentally that our algorithms are efficient, they can find ground truth in synthetic datasets and provide good results from real-world datasets. Finally, we present two case studies that show the usefulness of our problem.

FutureCite: Predicting Research Articles’ Impact Using Machine Learning and Text and Graph Mining Techniques

The growth in academic and scientific publications has increased very rapidly. Researchers must choose a representative and significant literature for their research, which has become challenging worldwide. Usually, the paper citation number indicates this paper’s potential influence and importance. However, this standard metric of citation numbers is not suitable to assess the popularity and significance of recently published papers. To address this challenge, this study presents an effective prediction method called FutureCite to predict the future citation level of research articles. FutureCite integrates machine learning with text and graph mining techniques, leveraging their abilities in classification, datasets in-depth analysis, and feature extraction. FutureCite aims to predict future citation levels of research articles applying a multilabel classification approach. FutureCite can extract significant semantic features and capture the interconnection relationships found in scientific articles during feature extraction using textual content, citation networks, and metadata as feature resources. This study’s objective is to contribute to the advancement of effective approaches impacting the citation counts in scientific publications by enhancing the precision of future citations. We conducted several experiments using a comprehensive publication dataset to evaluate our method and determine the impact of using a variety of machine learning algorithms. FutureCite demonstrated its robustness and efficiency and showed promising results based on different evaluation metrics. Using the FutureCite model has significant implications for improving the researchers’ ability to determine targeted literature for their research and better understand the potential impact of research publications.

Using Knowledge Graphs to Analyze the Characteristics and Trends of Forest Carbon Storage Research at the Global Scale

Research on forest carbon storage (FCS) is crucial for the sustainable development of human society given the context of global climate change. Previous FCS studies formed the science base of the FCS field but lacked a macrolevel knowledge summary. This study combined the scientometric mapping tool VOSviewer and multiple statistical models to conduct a comprehensive knowledge graph mining and analysis of global FCS papers (covering 101 countries, 1712 institutions, 5435 authors, and 276 journals) in the Web of Science database as of 2022, focusing on revealing the macro spatiotemporal pattern, multidimensional research status, and topic evolution process of FCS research at the global scale, so as to grasp the status of global FCS research more clearly and comprehensively, thereby facilitating the future decision-making and practice of researchers. The results showed the following: (1) In the past three decades, the number of FCS papers indicated an increasing trend, with a growth rate of 4.66/yr, particularly significant after 2010. These papers were mainly from Europe, the Americas, and Asia, while there was a huge gap between Africa, Oceania, and the above regions. (2) For the research status at the national, institutional, scholar, and journal levels, the USA, with 331 FCS papers and 18,653 total citations, was the most active and influential country in global FCS research; the United States Forest Service topped the influential ranking with 4115 citations; Grant M. Domke and Jerome Chave were the most active and influential FCS researchers globally, respectively. China’s activity (237 papers) and influence (5403 citations) ranked second, and the Chinese Academy of Sciences was the most active research institution in the world. Currently, FCS research is published in a growing number of journals, among which Forest Ecology and Management ranked first in the number of papers (154 papers) and citations (6374 citations). (3) In recent years, the keyword frequency of monitoring methods, driving factors, and reasonable management for FCS has increased rapidly, and many new related keywords have emerged, which means that researchers are not only focusing on the estimation and monitoring of FCS but also increasingly concerned about its driving mechanism and sustainable development.

Analyzing Time series by using Data mining

Algorithms and complex data analysis techniques are used in multiple fields that are expanding daily, and with it the challenges in facing multiple and more complex data types, and the directions of exploration research vary according to the diversity of these fields, and their use is increasing in the modern era in the field of artificial intelligence, which aims to facilitate human life in various fields. Mining of complex data types includes mining of time series, symbolic chains, and biological chains, in addition to mining of graphs, computer networks, mobile data, text mining, and data streams.

A Counting-based Approach for Efficient k-Clique Densest Subgraph Discovery

Densest subgraph discovery (DSD) is a fundamental topic in graph mining. It has been extensively studied in the literature and has found many real applications in a wide range of fields, such as biology, finance, and social networks. As a typical problem of DSD, the k-clique densest subgraph (CDS) problem aims to detect a subgraph from a graph, such that the ratio of the number of k-cliques over the number of its vertices is maximized. This problem has received plenty of attention in the literature, and is widely used in identifying larger ''near-cliques''. Existing CDS solutions, either k-core or convex programming based solutions, often need to enumerate almost all the k-cliques, which is very inefficient because real-world graphs usually have a vast number of k-cliques. To improve the efficiency, in this paper, we propose a novel framework based on the Frank-Wolfe algorithm, which only needs k-clique counting, rather than k-clique enumeration, where the former one is often much faster than the latter one. Based on the framework, we develop an efficient approximation algorithm, by employing the state-of-the-art k-clique counting algorithm and proposing some optimization techniques. We have performed extensive experimental evaluation on 14 real-world large graphs and the results demonstrate the high efficiency of our algorithms. Particularly, our algorithm is up to seven orders of magnitude faster than the state-of-the-art algorithm with the same accuracy guarantee.

Deep learning model for recommendation system using web of things based knowledge graph mining

Deep learning model for recommendation system using web of things based knowledge graph mining

Three-way graph convolutional network for multi-label classification in multi-label information system

The Graph Convolutional Neural Networks (GCNs) have demonstrated a powerful capacity for relation processing. To improve the representation learning capability of GCNs, numerous studies have begun to excavate latent topological graphs based on node features, aiming to obtain richer representations of nodes. However, these graph mining methods based on global metrics, such as Euclidean distance, lack descriptions of uncertainty and fine granularity, leading to the loss of structural information, which affects the final representation of nodes. Therefore, this paper proposes the Three-way Graph Convolutional Neural Network (TW-GCN), which is based on multi-granularity and three-way decision. First, from the perspective of multi-granularity, each attribute is analyzed as a granule, and we establish relations of advantage, disadvantage, and uncertainty for objects under each attribute granule. Three-way relation captures the complex connections between nodes from multiple perspectives, improves the comprehensive understanding of graph data, and avoids the information loss caused by traditional two-way processing. Second, a three-way graph convolutional layer is constructed to capture uncertainty and ambiguity by performing graph convolution on different topological graphs. The final node representation is obtained through message passing on the node’s neighbor graph, which considers not only the global structure, but also effectively captures local structural features. Finally, we employ the TW-GCN for multi-label classification within information systems to validate the model’s validity based on multiple indicators.

HUSM: High utility subgraph mining in single graph databases

Frequent subgraph mining (FSM) is a crucial research area with diverse applications. However, traditional FSM treats all subgraphs as equally important. In practical applications, some subgraphs may be frequent but not very valuable, while others may be infrequent but highly valuable. To address this issue, we introduce utility pattern mining into single graph mining, enabling the discovery of meaningful patterns based on user's interest rather than relying solely on support. We define a utility function in single graph databases and formally define the problem of high utility subgraph mining (HUSM). In the face of several challenges posed by this new problem, such as the absence of downward closure property and a large number of candidates, we design several utility upper bounds that satisfy the downward closure property. We then develop a HUSM algorithm to efficiently mine all high utility subgraphs in single graph databases. Additionally, we design a lossless concise representation for high utility subgraphs, which has fewer instances than the total number of high utility subgraphs and can derive all of them, making the mining results more representative. The experimental results demonstrate that our method exhibits excellent performance, and the concise representation plays a significant role in simplifying the mining results.