Graph Mining Research Articles

Important subgraph discovery using non-dominance criterion

Graph mining techniques have received a lot of attention to discover important subgraphs based on certain criteria. These techniques have become increasingly important due to the growing number of applications that rely on graph-based data. Some examples are: (i) microarray data analysis in bioinformatics, (ii) transportation network analysis, (iii) social network analysis. In this study, we propose a graph decomposition algorithm using the non-dominance criterion to identify important subgraphs based on two characteristics: edge connectivity and diameter. The proposed method uses a multi-objective optimization approach to maximize the edge connectivity and minimize the diameter. In a similar vein, identifying communities within a network can improve our comprehension of the network's characteristics and properties. Therefore, the detection of community structures in networks has been extensively studied. As a result, in this paper an innovative community detection method is presented based on our approach. The performance of the proposed technique is examined on both real-life and synthetically generated data sets.

Supervised chemical graph mining improves drug-induced liver injury prediction

Drug-induced liver injury (DILI) is the main cause of drug failure in clinical trials. The characterization of toxic compounds in terms of chemical structure is important because compounds can be metabolized to toxic substances in the liver. Traditional machine learning approaches have had limited success in predicting DILI, and emerging deep graph neural network (GNN) models are yet powerful enough to predict DILI. In this study, we developed a completely different approach, supervised subgraph mining (SSM), a strategy to mine explicit subgraph features by iteratively updating individual graph transitions to maximize DILI fidelity. Our method outperformed previous methods including state-of-the-art GNN tools in classifying DILI on two different datasets: DILIst and TDC-benchmark. We also combined the subgraph features by using SMARTS-based frequent structural pattern matching and associated them with drugs' ATC code.

Evaluation, Comparison and Monitoring of Multiparameter Systems by Unified Graphic Visualization of Activity Method on the Example of Learning Process

The article discusses the problem of visualization of complex multiparameter systems, defined by datasets on their structure, functional structure, and activity in the form of complex graphs and transition of traditional representation of the data acquired by graph mining to a compact image built by pictographic methods. In these situations, we propose using the Unified Graphic Visualization of Activity (UGVA) method for data concentration and structuring. The UGVA method allows coding in an anthropomorphic image of elements of graphs with data on structural and functional features of systems and overlaying these images with the data on the system’s activity using coloring and artifacts. The image can be composed in different ways: it can include the zone of integral evaluation parameters, segmented data axes of five types, and four types of symmetry. We describe the method of creating UGVA images, which consists of 13 stages: the parametric model is represented as a structural image that is converted to a basic image that is then detailed into the particular image by defining geometric parameters of the primitives and to the individualized image with the data about a particular object. We show how the individualized image can be overlaid with the operative data as color coding and artifacts and describe the principles of interpreting UGVA images. This allows solving tasks of evaluation, comparison, and monitoring of complex multiparameter systems by showing the decision-maker an anthropomorphic image instead of the graph. We describe a case study of using the UGVA method for visualization of data about an educational process: curricula and graduate students, including the data mined from the university’s learning management system at the Siberian Federal University for students majoring in “informatics and computing”. The case study demonstrates all stages of image synthesis and examples of their interpretation for situation assessment, monitoring, and comparison of students and curricula. It allowed for finding problematic moments in learning for individual students and their entire group by analyzing the development of their competence profiles and formulating recommendations for further learning. The effectiveness of the resulting images is compared to the other approaches: elastic maps and Chernoff faces. We discuss using graph mining to generate learning problems in order to lessen the workload of gathering raw data for the UGVA method and provide general recommendations for using the UGVA method based on our experience of supporting decision making.

Natural and Artificial Dynamics in Graphs: Concept, Progress, and Future.

Graph structures have attracted much research attention for carrying complex relational information. Based on graphs, many algorithms and tools are proposed and developed for dealing with real-world tasks such as recommendation, fraud detection, molecule design, etc. In this paper, we first discuss three topics of graph research, i.e., graph mining, graph representations, and graph neural networks (GNNs). Then, we introduce the definitions of natural dynamics and artificial dynamics in graphs, and the related works of natural and artificial dynamics about how they boost the aforementioned graph research topics, where we also discuss the current limitation and future opportunities.

Density Personalized Group Query

Research on new queries for finding dense subgraphs and groups has been actively pursued due to their many applications, especially in social network analysis and graph mining. However, existing work faces two major weaknesses: i) incapability of supporting personalized neighborhood density, and ii) inability to find sparse groups. To tackle the above issues, we propose a new query, called Density-Customized Social Group Query (DCSGQ), that accommodates the need for personalized density by allowing individual users to flexibly configure their social tightness (and sparseness) for the target group. The proposed DCSGQ is general due to flexible in configuration of neighboring social density in queries. We prove the NP-hardness and inapproximability of DCSGQ, formulate an Integer Program (IP) as a baseline, and propose an efficient algorithm, FSGSel-RR, by relaxing the IP. We then propose a fixed-parameter tractable algorithm with a performance guarantee, named FSGSel-TD, and further combine it with FSGSel-RR into a hybrid approach, named FSGSel-Hybrid, in order to strike a good balance between solution quality and efficiency. Extensive experiments on multiple large real datasets demonstrate the superior solution quality and efficiency of our approaches over existing subgraph and group queries.

An intelligent graph mining algorithm to analyze student performance in online learning

An intelligent graph mining algorithm to analyze student performance in online learning

Selective reinforcement graph mining approach for smart building energy and occupant comfort optimization

Optimizing Building energy consumption is a key solution to reducing their environmental impact. In this context, Information Technology can be harnessed by deploying sensors inside buildings, to collect relevant data about both energy consumed and occupant behavior, since occupants influence building appliances, such as HVAC, lights, and hot water tanks. Predicting room occupancy can be a solution to heat/cool rooms for instance. But, as prediction models are not often accurate, we may face situations where HVAC is activated while the rooms are empty or vice-versa, leading to either a waste of energy or a lack of occupants’ comfort. To predict user behavior, detect prediction errors, and correct the model, we introduce a graph mining-based optimization method that combines an occupant behavior prediction model and a selective reinforcement learning method, where error detection relies on sensors that detect real-time occupancy of rooms. We experimented with our approach on simulated data and results showed that, compared to conventional HVAC management, our model can reduce up to 57.8% of HVAC energy consumption, and provide up to 94.3% of occupants’ comfort when using the prediction method only, and up to 80.1% of HVAC energy consumption, and provide up to 97% of occupants’ comfort when using the reinforcement method to correct prediction errors.

Complex Prediction in Large PPI Networks Using Expansion and Stripe of Core Cliques.

The widespread availability and importance of large-scale protein-protein interaction (PPI) data demand a flurry of research efforts to understand the organisation of a cell and its functionality by analysing these data at the network level. In the bioinformatics and data mining fields, network clustering acquired a lot of attraction to examine a PPI network's topological and functional aspects. The clustering of PPI networks has been proven to be an excellent method for discovering functional modules, disclosing functions of unknown proteins, and other tasks in numerous research over the last decade. This research proposes a unique graph mining approach to detect protein complexes using dense neighbourhoods (highly connected regions) in an interaction graph. Our technique first finds size-3 cliques associated with each edge (protein interaction), and then these core cliques are expanded to form high-density subgraphs. Loosely connected proteins are stripped out from these subgraphs to produce a potential protein complex. Finally, the redundancy is removed based on the Jaccard coefficient. Computational results are presented on the yeast and human protein interaction dataset to highlight our proposed technique's efficiency. Predicted protein complexes of the proposed approach have a significantly higher score of similarity to those used as gold standards in the CYC-2008 and CORUM benchmark databases than other existing approaches.

Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning.

Drug repositioning, which involves the identification of new therapeutic indications for approved drugs, considerably reduces the time and cost of developing new drugs. Recent computational drug repositioning methods use heterogeneous networks to identify drug–disease associations. This review reveals existing network-based approaches for predicting drug–disease associations in three major categories: graph mining, matrix factorization or completion, and deep learning. We selected eleven methods from the three categories to compare their predictive performances. The experiment was conducted using two uniform datasets on the drug and disease sides, separately. We constructed heterogeneous networks using drug–drug similarities based on chemical structures and ATC codes, ontology-based disease–disease similarities, and drug–disease associations. An improved evaluation metric was used to reflect data imbalance as positive associations are typically sparse. The prediction results demonstrated that methods in the graph mining and matrix factorization or completion categories performed well in the overall assessment. Furthermore, prediction on the drug side had higher accuracy than on the disease side. Selecting and integrating informative drug features in drug–drug similarity measurement are crucial for improving disease-side prediction.

An edge feature aware heterogeneous graph neural network model to support tax evasion detection

Tax evasion is an illegal activity that causes severe losses of government revenues and disturbs the economic order. To alleviate this problem, decision support systems that enable tax authorities to detect tax evasion efficiently have been proposed. Recent researches tend to use graph to model the tax scenario and leverage graph mining techniques to conduct tax evasion detection, as so to make full use of the rich interactive information between taxpayers and improve the detection performance. However, a more favorable graph mining solution, graph neural networks, has not yet been thoroughly investigated in such settings, leaving space for further improvement. Therefore, in this paper, we propose a novel graph neural network model, named Eagle, to detect tax evasion under the heterogeneous graph. Specifically, based on the guidance of our designed metapaths, Eagle can extract more comprehensive features through a hierarchical attention mechanism that fully aggregates taxpayers’ features with their relations. We evaluate Eagle on real-world tax dataset. The extensive experimental results show that our model performs 15.71% better than state-of-the-art tax evasion detection methods in the classification scenario, while improves 5.22% in the anomaly detection scenario.

Line graph attention networks for predicting disease-associated Piwi-interacting RNAs.

PIWI proteins and Piwi-Interacting RNAs (piRNAs) are commonly detected in human cancers, especially in germline and somatic tissues, and correlate with poorer clinical outcomes, suggesting that they play a functional role in cancer. As the problem of combinatorial explosions between ncRNA and disease exposes gradually, new bioinformatics methods for large-scale identification and prioritization of potential associations are therefore of interest. However, in the real world, the network of interactions between molecules is enormously intricate and noisy, which poses a problem for efficient graph mining. Line graphs can extend many heterogeneous networks to replace dichotomous networks. In this study, we present a new graph neural network framework, line graph attention networks (LGAT). And we apply it to predict PiRNA disease association (GAPDA). In the experiment, GAPDA performs excellently in 5-fold cross-validation with an AUC of 0.9038. Not only that, it still has superior performance compared with methods based on collaborative filtering and attribute features. The experimental results show that GAPDA ensures the prospect of the graph neural network on such problems and can be an excellent supplement for future biomedical research.

Mining on Alzheimer’s diseases related knowledge graph to identity potential AD-related semantic triples for drug repurposing

BackgroundTo date, there are no effective treatments for most neurodegenerative diseases. Knowledge graphs can provide comprehensive and semantic representation for heterogeneous data, and have been successfully leveraged in many biomedical applications including drug repurposing. Our objective is to construct a knowledge graph from literature to study the relations between Alzheimer’s disease (AD) and chemicals, drugs and dietary supplements in order to identify opportunities to prevent or delay neurodegenerative progression. We collected biomedical annotations and extracted their relations using SemRep via SemMedDB. We used both a BERT-based classifier and rule-based methods during data preprocessing to exclude noise while preserving most AD-related semantic triples. The 1,672,110 filtered triples were used to train with knowledge graph completion algorithms (i.e., TransE, DistMult, and ComplEx) to predict candidates that might be helpful for AD treatment or prevention.ResultsAmong three knowledge graph completion models, TransE outperformed the other two (MR = 10.53, Hits@1 = 0.28). We leveraged the time-slicing technique to further evaluate the prediction results. We found supporting evidence for most highly ranked candidates predicted by our model which indicates that our approach can inform reliable new knowledge.ConclusionThis paper shows that our graph mining model can predict reliable new relationships between AD and other entities (i.e., dietary supplements, chemicals, and drugs). The knowledge graph constructed can facilitate data-driven knowledge discoveries and the generation of novel hypotheses.

Knowledge Automation Through Graph Mining, Convolution, and Explanation Framework: A Soft Sensor Practice

In industrial processes, data-driven soft sensors have played an important role for the effective process control, optimization, and monitoring. Deep learning technique has been widely used in soft sensor field in recent years for its excellent feature representation capability in spatial and temporal scales. However, the shortcomings for deep learning technique seriously hinder its application in industrial processes. For example, the knowledge cannot be added into the model, and the model prediction could not be well explained. To solve those problems, the graph mining, convolution, and explanation framework is proposed for knowledge automation in this article. Based on the equivalence analysis of the self-attention mechanism (SAM) and graph convolution (GC) operation, the spatial SAM is adopted for knowledge discovery from data directly. After that, the GC layer considering the relationship between process variables can utilize the knowledge for constructing soft sensor models. Besides, to explain which knowledge contributes to the final model prediction, the graph neural network explainer is designed for explaining the model output. Finally, the effectiveness and feasibility of the framework are evaluated on an industrial process, in which the knowledge discovered from the data is of great consistence with the prior knowledge, and the final explanation indicated that most of the knowledge is consistent with the prior knowledge contributed to the prediction.

Effective Graph Mining for Educational Data Mining and Interest Recommendation.

In order to fully understand and analyze the rules and cognitive characteristics of users' learning methods and, with the assistance of Internet and artificial acquaintance technology, to emphasize the integrity and degree of personalized education, a personalized graph-learning-based recommendation system including user portraits is proposed. System raking of data layers, data analysis responses, and recommendations for sum beds are seamless and collaboratively combined. The data layer consists of user data and a design library containing scholarship materials, study materials, and price sets. The data analysis framework is captured by rest and energy data represented by basic information, learning behavior, etc. We can provide perceptual and visual learning audio feedback. And thus witness computing should convey users' learning behavior rules through similarity analysis and mob algorithm. We further use TF-IDF to sequentially mine users' resource priorities and always bind personalized learning suggestions. The system has been applied to an online education platform supported by artificial intelligence technique, which can provide instructors and students with personalized portraits. We also proposed to learn audio feedback and data consulting services, typically during the hard work phase of the assistant semester.

Self-supervised deep geometric subspace clustering network

Graph mining has been widely studied to analyze real-world graph properties and applied to various applications. In particular, graph subspace clustering performance, defined as partitioning high-dimensional graph data into several clusters by finding minimum weights for the edges, has been consistently improved by exploiting deep learning algorithms with Euclidean features extracted from Euclidean domains (image datasets). Most subspace clustering algorithms tend to extract features from the Euclidean domain to identify graph characteristics and structures, and hence are limited for real-world data applications in non-Euclidean domains. This paper proposes a self-supervised deep geometric subspace clustering algorithm optimized for non-Euclidean high-dimensional graph data by emphasizing spatial features and geometric structures while simultaneously reducing redundant nodes and edges. Quantitative and qualitative experimental results verified the proposed approach is effective for graph clustering compared with previous state-of-the-art algorithms on public datasets.

A Survey on Role-Oriented Network Embedding

Recently, Network Embedding (NE) has become one of the most attractive research topics in machine learning and data mining. NE approaches have achieved promising performance in various graph mining tasks including link prediction and node clustering and classification. A wide variety of NE methods focus on the proximity of networks, they learn community-oriented embedding for each node, where the corresponding representations are similar if two nodes are closer to each other in the network. Meanwhile, there is another type of structural similarity, i.e., role-based similarity, which is completely different from and complementary to the proximity. In order to preserve the role-based structural similarity, the problem of role-oriented NE is raised. However, compared to the community-oriented NE, there are only a few role-oriented embedding approaches proposed recently. Although less explored, considering the importance of roles in analyzing networks and many applications that role-oriented NE can shed light on, it is necessary and timely to provide a comprehensive overview of existing role-oriented NE methods. In this review, we first clarify the differences between community-oriented and role-oriented network embedding. Afterward, we propose a general framework for understanding role-oriented NE and a two-level categorization to better classify existing methods. Then, we select some representative methods according to the proposed categorization and briefly introduce them by discussing their motivation, development, and differences. Moreover, we conduct comprehensive experiments to empirically evaluate these methods on a variety of role-related tasks including node classification and clustering (role discovery), top-k similarity search, and visualization using some widely used synthetic and real-world datasets. Finally, we further discuss the research trend of role-oriented NE from the perspective of applications and point out some potential future directions.

Joint hyperbolic and Euclidean geometry contrastive graph neural networks

Graph Neural Networks (GNNs) have demonstrated state-of-the-art performance in a wide variety of analytical tasks. Current GNN approaches focus on learning representations in a Euclidean space, which are effective in capturing non-tree-like structural relations, but they fail to model complex relations in many real-world graphs, such as tree-like hierarchical graph structure. This paper instead proposes to learn representations in both Euclidean and hyperbolic spaces to model these two types of graph geometries. To this end, we introduce a novel approach – Joint hyperbolic and Euclidean geometry contrastive graph neural networks (JointGMC). JointGMC is enforced to learn multiple layer-wise optimal combinations of Euclidean and hyperbolic geometries to effectively encode diverse complex graph structures. Further, the performance of most GNNs relies heavily on the availability of large-scale manually labeled data. To mitigate this issue, JointGMC exploits proximity-based self-supervised information in different geometric spaces (i.e., Euclidean, hyperbolic, and Euclidean-hyperbolic interaction spaces) to regularize the (semi-) supervised graph learning. Extensive experimental results on eight real-world graph datasets show that JointGMC outperforms eight state-of-the-art GNN models in diverse graph mining tasks, including node classification, link prediction, and node clustering tasks, demonstrating JointGMC’s superior graph representation ability.Code is available at https://github.com/chachatang/jointgmc.

Analisis Graph Mining dalam Penentuan Senyawa Dan Tanaman Obat Indonesia Sebagai Antihipertensi

Hypertension, also known as high blood pressure, is a degenerative disease whose prevalence tends to increase every year. People in Indonesia generally treat this disease by using a mixture of several types of herbal plants or so-called herbs. This study aims to find out the potential of compounds and plants that have effectiveness as antihypertensives by combining the concept of multicomponent-multi-target with a bioinformatics approach. This study aims to find out the potential of compounds and medicinal plants in Indonesia that have effectiveness as antihypertensives using graph mining analysis. Graph tracing techniques can be applied to search for compounds and plants related to target proteins in a disease. Patikan kebo (Euphorbia factor Ti2, betaAmyrin, alpha-Amyrin), mango (3-Carene, L-Histidine), cayenne pepper (Capsanthin, Capsorubin, Solasonine), papaya (L-Histidine, Sterol, Caffeine), and fenugreek ( L-Histidine) is the best plant that acts as an antihypertensive agent by targeting 4 proteins or about 80% of the total 5 significant proteins in hypertension.

Exploiting optimised communities in directed weighted graphs for link prediction

The most developing issue in analysing complex networks and graph mining is link prediction, which can be studied for both content and structural-based analysis in a social network. Link prediction deals with the prediction of missing links by determining whether a link can be created between two nodes in a future snapshot of a given directed weighted graph. Existing link prediction methods are only studied for unsigned graphs and work on principles of the common neighbourhood. However, the link prediction problem can also be studied for signed graphs where signed links can give an interesting insight into user associations. Obstruction of studies in this domain is caused by imbalance of class, i.e., positive links are frequent than negative ones, and forbearance of hidden communities. A signed network is a combination of dense and hidden communities. A hidden community structure is overlooked by majority of existing applications, taking dense community structure, i.e., one whole graph as input for developing a link prediction model. Hence, complete network information is required by majority of existing approaches, which seems unrealistic in modern social network analytics. In this article, we exploit hidden network communities to address link prediction problem in the signed network, focusing on negative links. A number of observation were made regarding negative links and a principle ensemble framework, i.e., ENeLp, is proposed, having two phases, i.e, network embedding and classifier prediction. Using a probabilistic embedding framework, network representation of hidden signed communities is learned, which were then passed to a learning classifier to predict negative links, keeping intact the ensemble framework. Despite the limited availability of signed network datasets, an extensive experimental study was performed to evaluate ENeLp pertinency, robustness, and scalability. The performance result shows that ENeLp can be a promising consideration for addressing link prediction tasks in signed networks and gives encouraging results.

Contributors

Previous articleNext article FreeContributorsPDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinked InRedditEmailQR Code SectionsMoreLuciana de Souza Leão is assistant professor of sociology at the University of Michigan. She is a political and comparative sociologist with broad interests in knowledge-making processes, social inequalities, and the state.Pablo Gastón is assistant professor of sociology at the University of Michigan. His research focuses on changing forms of economic conflict in the American labor movement.Danya Lagos is assistant professor in the Department of Sociology at the University of California, Berkeley. They specialize in the relationship between gender, social change, and demography.Grégoire Mallard is professor of anthropology and sociology and director of research at the Graduate Institute of International and Development Studies, Geneva. His ERC grant (2017–22), titled “Bombs, Banks and Sanctions,” extends his prior work on transnational lawmaking and forms of economic exchange.Jin Sun is a Ph.D. candidate in anthropology and sociology at the Graduate Institute of International and Development Studies, where he is a researcher in the Global Governance Centre. He passed the bar exam in Beijing, China, and directed studies at the Hague Academy of International Law. His research focuses on sanctions, global governance, and law and globalization.Emily Rauscher is associate professor of sociology at Brown University and a faculty affiliate of the Population Studies and Training Center and the Annenberg Institute for School Reform. Her work examines social returns to education, including when and how school funding improves equality of opportunity.Yifan Shen is a Ph.D. candidate in sociology at Brown University. He studies family demography, education, and income inequality.Daniel Silver is professor of sociology at the University of Toronto Scarborough. He received his Ph.D. from the Committee on Social Thought at the University of ChicagoClayton Childress is associate professor in the Department of Sociology at the University of Toronto. He is the author of Under the Cover: The Creation, Production, and Reception of a Novel (Princeton University Press, 2017), which won the 2018 Mary Douglas Prize for best book in the sociology of culture.Monica Lee is a Computational Social Science Director at Meta, where she leads the Political & Organizational Science team. She earned her Ph.D. in Sociology from the University of Chicago and has published works on quantitative methods for measuring culture, text mining, musical taste, graph mining, big data, and ethics.Adam Slez is assistant professor in the Department of Sociology at the University of Virginia. His recent book, The Making of the Populist Movement: State, Market, and Party on the Western Frontier (Oxford University Press, 2020), examines the origins of electoral populism in the United States during the late 19th century.Fabio Dias has an engineering degree and master’s from the University of Campinas, Brazil, with a Ph.D. in Computer Science from Université Paris-Est. After several postdoctoral fellowships in data analysis and visualization, including at the University of São Paulo, New York University, and the University of Toronto, he is now a cloud architect for StateStreet. Previous articleNext article DetailsFiguresReferencesCited by American Journal of Sociology Volume 128, Number 1July 2022 Article DOIhttps://doi.org/10.1086/720956 Views: 518Total views on this site © 2022 The University of Chicago. All rights reserved.PDF download Crossref reports no articles citing this article.