Graph Data Mining Research Articles

Few-shot Learning for Heterogeneous Information Networks

Heterogeneous information networks (HINs) are a key resource in many domain-specific retrieval and recommendation scenarios and in conversational environments. Current approaches to mining graph data often rely on abundant supervised information. However, supervised signals for graph learning tend to be scarce for a new task and only a handful of labeled nodes may be available. Meta-learning mechanisms are able to harness prior knowledge that can be adapted to new tasks. In this article, we design meta-learning framework for heterogeneous information networks ( META-HIN ), for few-shot learning problems on HINs. To the best of our knowledge, we are among the first to design a unified framework to realize the few-shot learning of HINs and facilitate different downstream tasks across different domains of graphs. Unlike most previous models, which focus on a single task on a single graph, META-HIN is able to deal with different tasks (node classification, link prediction, and anomaly detection are used as examples) across multiple graphs. Subgraphs are sampled to build the support and query set. Before being processed by the meta-learning module, subgraphs are modeled via a structure module to capture structural features. Then, a heterogeneous Graph Neural Network module is used as the base model to express the features of subgraphs. We also design a Generative Adversarial Network-based contrastive learning module that is able to exploit unsupervised information of the subgraphs. In our experiments, we fuse several datasets from multiple domains to verify META-HIN ’s broad applicability in a multiple-graph scenario. META-HIN consistently and significantly outperforms state-of-the-art alternatives on every task and across all datasets that we consider.

Research on the design of teaching aid resource system based on unsupervised representation learning

In the absence of manual annotation information, using only the topology information of the graph to achieve unsupervised graph alignment has always been one of the important challenges faced by graph data mining, especially in the large-scale graph alignment task, the discovery of initial seed nodes and the low computational efficiency have always been a problem. This paper proposes a large-scale unsupervised graph-aligned physical education teaching resource system framework based on topological representation learning, which is highly scalable and adaptable. Firstly, representative subgraphs are selected from the graph to be matched as seed node candidate sets, and the local topology information is used to calculate to obtain highly reliable seed node matching results; then, the obtained seed nodes are used to integrate the graph to be matched, and an efficient unsupervised representation learning algorithm is proposed to map the fusion graph to a unified vector space; finally the obtained node vectors are used to achieve the alignment of large-scale graphs. The proposed technique shows the shortest time when dealing with large-scale graph alignment tasks, and at the same time reaches the highest level of accuracy of alignment results, in addition, the performance of the physical education auxiliary resource system algorithm has the least impact on the difference of graph structure.

DeepInsight: Topology Changes Assisting Detection of Adversarial Samples on Graphs

With the rapid development of artificial intelligence, a number of machine learning algorithms, such as graph neural networks (GNNs), have been proposed to facilitate network analysis or graph data mining. Although effective, recent studies show that these advanced methods may suffer from adversarial attacks, i.e., they may lose effectiveness when only a small fraction of links are unexpectedly changed. This article investigates three well-known adversarial attack methods, i.e., Nettack, Meta Attack, and GradArgmax. It is found that different attack methods have their specific attack preferences on changing the target network structures. Such attack patterns are further verified by experimental results on some real-world networks, revealing that, generally, the top-4 most important network attributes on detecting adversarial samples suffice to explain the preference of an attack method. Based on these findings, the network attributes are utilized to design machine learning models for adversarial sample detection and attack method recognition with outstanding performance.

A one-step graph clustering method on heterogeneous graphs via variational graph embedding

<abstract><p>Graph clustering is one of the fundamental tasks in graph data mining, with significant applications in social networks and recommendation systems. Traditional methods for clustering heterogeneous graphs typically involve obtaining node representations as a preliminary step, followed by the application of clustering algorithms to achieve the final clustering results. However, this two-step approach leads to a disconnection between the optimization of node representation and the clustering process, making it challenging to achieve optimal results. In this paper, we propose a graph clustering approach specifically designed for heterogeneous graphs that unifies the optimization of node representation and the clustering process for nodes in a heterogeneous graph. We assume that the relationships between different meta-paths in the heterogeneous graph are mutually independent. By maximizing the joint probability of meta-paths and nodes, we derive the optimization objective through variational methods. Finally, we employ backpropagation and reparameterization techniques to optimize this objective and thereby achieve the desired clustering results. Experiments conducted on multiple real heterogeneous datasets demonstrate that the proposed method is competitive with existing methods.</p></abstract>

APTM: Structurally Informative Network Representation Learning

Network representation learning algorithms provide a method to map complex network data into low-dimensional real vectors, aiming to capture and preserve structural information within the network. In recent years, these algorithms have found widespread applications in tasks such as link prediction and node classification in graph data mining. In this work, we propose a novel algorithm based on an adaptive transfer probability matrix. We use a deep neural network, comprising an autoencoder, to encode and reduce the dimensionality of the generated matrix, thereby encoding the intricate structural information of the network into low-dimensional real vectors. We evaluate the algorithm's performance through node classification, and in comparison with mainstream network representation learning algorithms, our proposed algorithm demonstrates favorable results. It outperforms baseline models in terms of micro-F1 scores on three datasets: PPI, Citeseer, and Wiki.

Graph embedding and geometric deep learning relevance to network biology and structural chemistry.

Graphs are used as a model of complex relationships among data in biological science since the advent of systems biology in the early 2000. In particular, graph data analysis and graph data mining play an important role in biology interaction networks, where recent techniques of artificial intelligence, usually employed in other type of networks (e.g., social, citations, and trademark networks) aim to implement various data mining tasks including classification, clustering, recommendation, anomaly detection, and link prediction. The commitment and efforts of artificial intelligence research in network biology are motivated by the fact that machine learning techniques are often prohibitively computational demanding, low parallelizable, and ultimately inapplicable, since biological network of realistic size is a large system, which is characterised by a high density of interactions and often with a non-linear dynamics and a non-Euclidean latent geometry. Currently, graph embedding emerges as the new learning paradigm that shifts the tasks of building complex models for classification, clustering, and link prediction to learning an informative representation of the graph data in a vector space so that many graph mining and learning tasks can be more easily performed by employing efficient non-iterative traditional models (e.g., a linear support vector machine for the classification task). The great potential of graph embedding is the main reason of the flourishing of studies in this area and, in particular, the artificial intelligence learning techniques. In this mini review, we give a comprehensive summary of the main graph embedding algorithms in light of the recent burgeoning interest in geometric deep learning.

Graph structure reforming framework enhanced by commute time distance for graph classification

As a graph data mining task, graph classification has high academic value and wide practical application. Among them, the graph neural network-based method is one of the mainstream methods. Most graph neural networks (GNNs) follow the message passing paradigm and can be called Message Passing Neural Networks (MPNNs), achieving good results in structural data-related tasks. However, it has also been reported that these methods suffer from over-squashing and limited expressive power. In recent years, many works have proposed different solutions to these problems separately, but none has yet considered these shortcomings in a comprehensive way. After considering these several aspects comprehensively, we identify two specific defects: information loss caused by local information aggregation, and an inability to capture higher-order structures. To solve these issues, we propose a plug-and-play framework based on Commute Time Distance (CTD), in which information is propagated in commute time distance neighborhoods. By considering both local and global graph connections, the commute time distance between two nodes is evaluated with reference to the path length and the number of paths in the whole graph. Moreover, the proposed framework CTD-MPNNs (Commute Time Distance-based Message Passing Neural Networks) can capture higher-order structural information by utilizing commute paths to enhance the expressive power of GNNs. Thus, our proposed framework can propagate and aggregate messages from defined important neighbors and model more powerful GNNs. We conduct extensive experiments using various real-world graph classification benchmarks. The experimental performance demonstrates the effectiveness of our framework. Codes are released on https://github.com/Haldate-Yu/CTD-MPNNs.

The statistical nature of h-index of a network node and its extensions

Evaluating the importance of a network node is a crucial task in network science and graph data mining. H-index is a popular centrality measure for this task, however, there is still a lack of its interpretation from a rigorous statistical aspect. Here we rewrite h-index in a new way based on order statistics, which allows us to obtain a new family of centrality indices by generalizing the h-index along this direction. The theoretical and empirical evidence shows that such a statistical interpretation enables us to obtain a general and versatile framework for quantifying the importance of a network node. Under this framework, many new centrality indices can be derived and some of which can be more accurate and robust than h-index. We believe that this research opens up new avenues for developing more effective indices for node importance quantification from a viewpoint that still remains unexplored.

Distributed structural clustering on large graph

SummaryGraph clustering is a primitive operation for graph data mining. It plays an important role to reveal community clusters, hubs, and outliers in complex networks. There are several graph clustering algorithms have been proposed based on the well‐studied SCAN algorithm in recent years. However, SCAN and its improved sequential variants are prohibitively slow due to their iterative computations. The parallel variants are focusing on improving the efficiency of graph clustering by utilizing multi‐cores computer architectures on single computing node with complex optimization techniques. Therefore, SCAN and its variants are not suitable for processing very large graphs due to the limitations of memory size and storage volume on a single node. In this article, we proposed a distributed parallel structural clustering algorithm using MapReduce. In order to improve the efficiency further, we proposed optimization techniques including partition based clustering and simplified combination with labels to accelerate the operations. We conducted extensive experiments on real world datasets. The experimental results showed our algorithm is high efficiency and scales well under different settings.

A time sequence coding based node-structure feature model oriented to node classification

Graph data mining is an important method for managing complex systems in artificial intelligence (AI). As an important branch of graph data mining, node classification is widely applied in paper classification in citation networks, user classification in social networks, etc. At present, many graph neural networks (GNNs) have been proposed to realize node classification by obtaining node embeddings encoded by aggregating neighborhood features. Most GNNs use one-hot encoding as the initial node feature when there is no node attribute in the graph. However, one-hot encoding is a global encoding technique that cannot reflect the environmental context of specific nodes. To this end, we proposed a graph structure sequential coding (GSSC) model, including a learnable node-structure feature X, sampler, encoder and classifier, to obtain the structural embeddings which can better reflect the topological structure of non-attribute graphs. For non-attribute graphs, we utilized the learnable feature X as the initial node-structure feature, which could be simultaneously trained with the GSSC model. In addition, we used a decoupling scheme to separate the sampling and encoding processes of GSSC. Therefore, we could use different samplers for sparse and dense graphs. In addition, different sampling orders (i.e., temporality) in node sequences could reflect different semantic associations between the nodes. Therefore, we innovatively employed time sequence models (TSMs) as encoders to encode node sequences with temporality. Based on these TSMs, semantic information of the head node in the node sequence can be captured. The experimental results on five datasets confirmed that our GSSC model performs better than other representative methods in node classification for graphs without node attributes.

Graph classification based fault detection in nuclear power plants with graph formulation

In the routine production and operation of nuclear power plants (NPPs), safety management is extremely important, and consequently, a fault detection system yielding high accuracy could provide effective decision support and prevent the accidents to the maximum extent. Most of existing studies are based on the analysis of time-series data, while researches oriented to the implementation of graph data mining are barely seen. In this paper, we propose an improved fault detection method to address this task from the perspective of graph representation learning. By calculating the similarity between indicators, the multivariate temporal sequences of the running characteristics of NPPs have been formulated as a group of graphs with corresponding labels, on which the graph convolutional network (GCN) has been introduced to detect fault samples in the way of graph classification. Compared to conventional supervised learning classifiers, our proposed methodology performs more superior identification of major faults, and the sensitivity regarding significant parameter or settings in the model design has been analyzed to investigate their optimal arrangements. The findings in this article not only present the promising prospects of introducing graph learning techniques into safety management, also indicate that various kinds of digital signals could be reformulated into graph dataset based on which the original task could be executed more effectively in the way of graph representation learning.

A flexible framework for multiple-role discovery in real networks

In complex networks, the role of a node is based on the aggregation of structural features and functions. However, in real networks, it has been observed that a single node can have multiple roles. Here, the roles of a node can be defined in a case-by-case manner, depending on the graph data mining task. Consequently, a significant obstacle to achieving multiple-role discovery in real networks is finding the best way to select datasets for pre-labeling. To meet this challenge, this study proposes a flexible framework that extends a single-role discovery method by using domain adversarial learning to discover multiple roles for nodes. Furthermore, we propose a method to assign sub-networks, derived through community extraction methods, to a source network and a validation network as training datasets. Experiments to evaluate accuracy conducted on real networks demonstrate that the proposed method can achieve higher accuracy and more stable results.

LRP2A: Layer-wise Relevance Propagation based Adversarial attacking for Graph Neural Networks

Graph Neural Networks (GNNs) are widely utilized for graph data mining, attributable to their powerful feature representation ability. Yet, they are prone to adversarial attacks with only slight perturbations of input data, limiting their applicability to critical applications. Vulnerability analysis of GNNs is thus essential if more robust models are to be developed. To this end, a Layer-wise Relevance Propagation based Adversarial attacking (LRP2A) model is proposed11Data and code are available at https://github.com/duiyady/LRP2A.. Specifically, to facilitate applying LRP to the “black-box” victim model, we train a surrogate model based on a sophisticated re-weighting network. The LRP algorithm is then leveraged for unraveling “contributions” among the nodes in the downstream classification task. Furthermore, the graph adversarial attacking algorithm is intentionally designed to be both interpretable and efficient. Experimental results prove the effectiveness of the proposed attacking model on GNNs for node classification. Additionally, the adoption of LRP2A allows the choice of the adversarial attacking strategies on the GNN interpretable, which in turn can gain deeper insights on the GNN’s vulnerability.

Densest subgraph discovery on large graphs

As one of the most fundamental problems in graph data mining, the densest subgraph discovery (DSD) problem has found a broad spectrum of real applications, such as social network community detection, graph index construction, regulatory motif discovery in DNA, fake follower detection, and so on. Theoretically, DSD closely relates to other fundamental graph problems, such as network flow and bipartite matching. Triggered by these applications and connections, DSD has garnered much attention from the database, data mining, theory, and network communities. In this tutorial, we first highlight the importance of DSD in various applications and the unique challenges that need to be addressed. Subsequently, we classify existing DSD solutions into several groups, which cover around 50 research papers published in many well-known venues (e.g., SIGMOD, PVLDB, TODS, WWW), and conduct a thorough review of these solutions in each group. Afterwards, we analyze and compare the models and solutions in these works. Finally, we point out a list of promising future research directions. We believe that this tutorial not only helps researchers have a better understanding of existing densest subgraph models and solutions, but also provides them insights for future study.

Mining Stable Communities in Temporal Networks by Density-Based Clustering

Community detection is a fundamental task in graph data mining. Most existing studies in contact networks, collaboration networks, and social networks do not utilize the temporal information associated with edges for community detection. In this article, we study a problem of finding stable communities in a temporal network, where each edge is associated with a timestamp. Our goal is to identify the communities in a temporal network that are stable over time. To efficiently find the stable communities, we develop a new community detection algorithm based on the density-based graph clustering framework. We also propose several carefully-designed pruning techniques to significantly speed up the proposed algorithm. We conduct extensive experiments on four real-life temporal networks to evaluate our algorithm. The results demonstrate the effectiveness and efficiency of the proposed algorithm.

Decision Support Algorithm for Discipline Construction of Comparative Pedagogy Based on Evolutionary Graph Data Mining

The traditional pedagogy discipline construction decision support algorithm has the problems of poor discipline construction decision satisfaction, high decision time, and low decision recall rate. Therefore, a decision support algorithm for discipline construction of comparative pedagogy based on evolutionary graph data mining is designed. First, the programme call graph is created based on the programme execution path, and then the graph reduction method is used to decrease the call graph set and create the weighted behavior graph set. The original graph set is then reduced using the subtree reduction procedure. The interference of weights in the graph must be eliminated while mining closed subgraphs using a close graph method. The most frequent subgraph of comparative pedagogy discipline construction is then mined, and an SVM classifier is created to accomplish information mining of comparative pedagogy discipline construction in evolutionary graph data mining. Then, using the complete weighing approach, the attribute classification of comparative pedagogy discipline construction is accomplished, and the decision weight of comparative pedagogy discipline construction is established. Finally, the weight distribution scheme of comparative pedagogy discipline construction is obtained by using European distance, so as to realize the decision support of comparative pedagogy discipline construction. The experimental results show that the decision-making satisfaction of comparative pedagogy discipline construction of this method is 97.32%, the decision-making time is only 0.9 min, and the decision-making recall rate is as high as 98.66%, indicating that the decision-making effect of comparative pedagogy discipline construction of this method is good.

Special Issue of Graph Data Management, Mining, and Applications (APWeb-WAIM 2020)

Special Issue of Graph Data Management, Mining, and Applications (APWeb-WAIM 2020)

Link prediction based on higher-order structure extraction and autoencoder learning in directed networks

Link prediction, which aims to estimate the likelihood of links based on the observed links and/or node attributes, is a common task in complex network analysis and graph data mining. Modeling and exploring high-order structure, also called network motif, are essential for understanding the fundamental structure that control and mediate the behavior of various complex systems. Most existing link prediction methods do not fully consider the high-order structure in the network. In addition, they mainly focus on undirected networks and therefore ignore the directionality of links. To this end, we propose a novel Autoencoder model based on high-order structure to solve the problems of existing link prediction methods. Specifically, we first provide an efficient motif adjacency matrix learning algorithm, which can be used to extract high-order structure in directed networks, and construct multiple motif adjacency matrices. Then, we extend the Graph Autoencoder (GAE) and Variational Autoencoder (VAE) frameworks to solve the link prediction problem in directed networks, which consists of three core modules. (i) First (Node Embedding): a novel encoder scheme is presented to learn the node embeddings from each motif adjacency matrix; (ii) Second (Information Fusion): a robust attention scheme is further introduced to aggregate information of node embeddings learned from multiple motif adjacency matrices to form the final semantically rich node embedding matrix; (iii) Third (Link Prediction): an efficient decoder scheme is finally proposed to reconstruct the target directed network in a multiscale way, by jointly utilizing node embeddings and PageRank centrality, the directions of links can be effectively inferred based on the classic gravitational heuristic formula. Extensive experiments were applied on various types of directed networks to validate the performance of our proposed approach through comparisons with the state-of-the-art link prediction technologies.

CLPVG: Circular limited penetrable visibility graph as a new network model for time series.

A visibility graph transforms time series into graphs, facilitating signal processing by advanced graph data mining algorithms. In this paper, based on the classic limited penetrable visibility graph method, we propose a novel mapping method named circular limited penetrable visibility graph, which replaces the linear visibility line in limited penetrable visibility graph with nonlinear visibility arc for pursuing more flexible and reasonable mapping of time series. Tests on degree distribution and some common network features of the generated graphs from typical time series demonstrate that our circular limited penetrable visibility graph can effectively capture the important features of time series and show higher robust classification performance than the traditional limited penetrable visibility graph in the presence of noise. The experiments on real-world time-series datasets of radio and electroencephalogram signals also suggest that the structural features provided by a circular limited penetrable visibility graph, rather than a limited penetrable visibility graph, are more useful for time-series classification, leading to higher accuracy. This classification performance can be further enhanced through structural feature expansion by adopting subgraph networks. All of these results demonstrate the effectiveness of our circular limited penetrable visibility graph model.

Data Mining Between Classical and Modern Applications: A Review

Data mining (DM) is an incredible innovation with extraordinary potential to help organizations centre around the main data in the information they have gathered about the conduct of their clients and likely clients. It finds data inside the information that inquiries and reports can't viably uncover. Overall, DM (to a great extent called information or data revelation) is the route toward analysing data according to substitute perspectives and summarizing it into significant information - information that can be used to assemble pay, diminishes costs, or both. DM writing computer programs is one of different logical gadgets for separating data. It grants customers to separate data from a wide scope of estimations or focuses, organize it, and summarize the associations perceived. In reality, DM is the path toward finding associations or models among numerous fields in enormous social datasets. Procedures used in DM measure come from a mix of computational strategies including Artificial Intelligence (AI), Statistics, Machine Learning (ML), and Database (DB) Systems. Aside from the centre techniques used to do the investigation, the cycle of DM can include different pre-handling ventures preceding executing the mining method. Also, a post-preparing stage is normally utilized to picture the aftereffects of the investigation (for example perceived examples or recovered data) in an instinctive and simple to-impart way. From a wide perspective, there are two significant standards of methods: expectation and information disclosure. It includes four sub-groups: a) Classification, Prediction and Regression, b) Clustering, c) Association Rule and Sequence Pattern Mining, and d) Outliers and Anomaly Detection. What's more, there are some generally new and energizing zones of information investigation, for example, spatial DM and graph DM that have been made conceivable through the structure squares of DM techniques. This survey not just advantages analyst to create solid examination subjects and distinguish gaps in the research areas yet additionally helps experts for data mining and Big Data (BD) software framework advancement.