Visual Analytics for Understanding Relationships between Entities

Abstract. Network analysis has become an important approach in studying complex spatiotemporal behaviour within geophysical observation and simulation data. This new field produces increasing numbers of large geo-referenced networks to be analysed. Particular focus lies currently on the network analysis of the complex statistical interrelationship structure within climatological fields. The standard procedure for such network analyses is the extraction of network measures in combination with static standard visualisation methods. Existing interactive visualisation methods and tools for geo-referenced network exploration are often either not known to the analyst or their potential is not fully exploited. To fill this gap, we illustrate how interactive visual analytics methods in combination with geovisualisation can be tailored for visual climate network investigation. Therefore, the paper provides a problem analysis relating the multiple visualisation challenges to a survey undertaken with network analysts from the research fields of climate and complex systems science. Then, as an overview for the interested practitioner, we review the state-of-the-art in climate network visualisation and provide an overview of existing tools. As a further contribution, we introduce the visual network analytics tools CGV and GTX, providing tailored solutions for climate network analysis, including alternative geographic projections, edge bundling, and 3-D network support. Using these tools, the paper illustrates the application potentials of visual analytics for climate networks based on several use cases including examples from global, regional, and multi-layered climate networks.

Modeling, analysis, control, and management of complex social networks represent an important area of interdisciplinary research in an advanced digitalized world. In the last decade social networks have produced significant online applications which are running on top of a modern Internet infrastructure and have been identified as major driver of the fast growing Internet traffic. The "Second International Workshop on Modeling, Analysis and Management of Social Networks and Their Applications" (SOCNET 2018) held at Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, on February 28, 2018, has covered related research issues of social networks in modern information society. The Proceedings of SOCNET 2018 highlight the topics of a tutorial on "Network Analysis in Python" complementing the workshop program, present an invited talk "From the Age of Emperors to the Age of Empathy", and summarize the contributions of eight reviewed papers. The covered topics ranged from theoretical oriented studies focusing on the structural inference of topic networks, the modeling of group dynamics, and the analysis of emergency response networks to the application areas of social networks such as social media used in organizations or social network applications and their impact on modern information society. The Proceedings of SOCNET 2018 may stimulate the readers' future research on monitoring, modeling, and analysis of social networks and encourage their development efforts regarding social network applications of the next generation.

Subgraph Isomorphism is a fundamental problem in graph theory. It has many applications in social network analysis, molecular investigations, knowledge graphs, etc. Given a Query Graph and a Data Graph, the target of Subgraph Isomorphism, i.e., Subgraph Matching, is to determine if this Query Graph is isomorphic to any subgraph of the Data Graph. This work proposes a new type of Query Graph, combined with multiple general Query Graphs. We call it Compulsory-Optional Query Graph (CO Query Graph). This new type of Query Graph contains all the vertices in the combined general Query Graph, and each vertex corresponds to a search priority. Based on CO Query Graph, the previous multiple match processes can be reduced to one. It tremendously improves search efficiency. The Subgraph Isomorphism based on this new kind of Query Graph is an extension and improvement of the previous Subgraph Isomorphism studies. We propose a backtracking-pruning-based CO solver (BPC). This algorithm builds on the backtracking-pruning framework. BPC modifies the output criterion and matching conditions to satisfy the CO query context. A case study of real-world graph data illustrates that BPC built on CO Query Graph is more efficient than conventional Query Graphs. To verify the effectiveness of our method, we conducted experiments on the synthetic graph and real-world data. The results show that the BPC can significantly reduce the search space and improve the search efficiency in the recursive calls and the response time. Experiments resulting from synthetic graph data analysis allow us to primarily identify the critical factor that affects the efficiency of the BPC primarily.

Graph-based approaches for fake account detection is one of the important means to fight against fake accounts’ attacks on social networks. With the growth of the scale of social networks, more and more researchers begin to use the graph computing framework to boost their detection algorithms. We make detailed analyses of social networks’ graph data and state-of-the-art graph computing frameworks, and find that some techniques of the current graph computing systems are overgeneralized and suboptimal, which means they only focus on how to design a graph processing framework on general graphs but miss the optimization of social networks graphs. So, in this paper we propose QuickSquad, a graph computing system on a single server which is specific to the optimization of social networks graph structures. QuickSquad uses the method of ”divide and rule” instead of overgeneralization. First, we divide the graph structure data into the heavy set and the light set according to the out-degree of vertices. Then, we 1) store them with different formats, 2) process them with edge-based updating and vertex-based updating appropriately in a two-phase processing model, 3) apply two selective scheduler strategies of different level, i.e. vertex-level and file-level, and 4) provide four cache priorities when the memory is not enough to cache all data. Finally, we implement two detection methods, dSybilRank and dCOLOR, on our system, and the experiments demonstrate that our system can increase the performance up to 5.91X (from 1.14X) compared with the performance of the current graph computing systems, like GridGraph.

The social network model is powerful enough to provide for the analysis and study of a variety of application domains from daily life, including health care and health informatics. After the widespread appearance of automated tools capable of deriving and analyzing social networks, social network analysis (SNA) and mining in the health care domain has recently received considerable attention for its key role in understanding how various bodies within the health care system form communities and how they are socially connected with each other. This understanding helps enhance the organizational structures and process flows, among others. In this article, we show how SNA techniques can solve issues in the medical referral system in the Canadian health care system and the like, by analyzing the social network of general practitioners (GPs) and specialists (SPs). One of the main targets is to optimize the communication between GPs and SPs with hopes of decreasing the waiting time of patients to be seen by SPs. Various SNA and mining techniques are described and analyzed, backed by reporting some experimental results.

The growing number of available data graphs in the form of RDF Linked Data enables the development of semantic exploration applications in many domains. Often, the users are not domain experts and are therefore unaware of the complex knowledge structures represented in the data graphs they interact with. This hinders users’ experience and effectiveness. Our research concerns intelligent support to facilitate the exploration of data graphs by users who are not domain experts. We propose a new navigation support approach underpinned by the subsumption theory of meaningful learning, which postulates that new concepts are grasped by starting from familiar concepts which serve as knowledge anchors from where links to new knowledge are made. Our earlier work has developed several metrics and the corresponding algorithms for identifying knowledge anchors in data graphs. In this paper, we assess the performance of these algorithms by considering the user perspective and application context. The paper address the challenge of aligning basic level objects that represent familiar concepts in human cognitive structures with automatically derived knowledge anchors in data graphs. We present a systematic approach that adapts experimental methods from Cognitive Science to derive basic level objects underpinned by a data graph. This is used to evaluate knowledge anchors in data graphs in two application domains - semantic browsing (Music) and semantic search (Careers). The evaluation validates the algorithms, which enables their adoption over different domains and application contexts.

<p><em>The rapid growth of digital recruitment platforms in Indonesia has generated a large volume of user content on social media, serving as a vital data source for Social Customer Relationship Management (Social CRM) strategies. Consequently, the strategic insights that can be drawn may be limited. This study applied an integrated analytical approach combining Social Network Analysis (SNA) and lexicon-based sentiment analysis to evaluate public interactions regarding Jobstreet, Glints, and Dealls. The research methodology involved collecting data from platform X (previously known as Twitter) during the period of April 1-30, 2025, which was then analyzed using SNA with Gephi to identify influential actors through centrality metrics, alongside sentiment analysis to measure emotional polarity. The main findings revealed that Jobstreet possessed the healthiest conversational ecosystem, characterized by positive and neutral sentiment from its central actors. Glints exhibited sentiment polarization, and Dealls showed reputational vulnerability due to dominant negative sentiment from its influential users. It was concluded that the integration of these two methods provides a robust framework for designing more responsive and data-driven Social CRM strategies.</em></p><p><strong><em>Keywords</em></strong><strong><em>:</em></strong><em> Social Network Analysis; Sentiment Analysis; Social CRM; Digital Recruitment; Lexicon-Based Features.</em><em></em></p><p align="center"><strong>Abstrak</strong></p><p>Perkembangan pesat platform rekrutmen digital di Indonesia telah menghasilkan volume besar konten pengguna di media sosial, yang menjadi sumber data vital untuk strategi <em>Social Customer Relationship Management</em> (<em>Social</em> CRM). Sehingga hal ini dapat menyebabkan insight strategis yang bisa diambil menjadi terbatas. Penelitian ini menerapkan pendekatan analitis terpadu yang menggabungkan <em>Social Network Analysis</em> (SNA) dan analisis sentimen berbasis leksikon untuk mengevaluasi interaksi publik mengenai Jobstreet, Glints, dan Dealls. Metodologi penelitian melibatkan pengumpulan data dari platform X (sebelumnya dikenal dengan Twitter) selama periode 1-30 April 2025, yang kemudian dianalisis menggunakan SNA dengan Gephi untuk mengidentifikasi aktor berpengaruh melalui metrik sentralitas, serta analisis sentimen untuk mengukur polaritas emosional. Temuan utama mengungkapkan bahwa Jobstreet memiliki ekosistem percakapan paling sehat, ditandai oleh sentimen positif dan netral dari aktor-aktor sentralnya. Sebaliknya, Glints menunjukkan polarisasi sentimen, dan Dealls menunjukkan kerentanan reputasi karena sentimen negatif yang dominan dari para pengguna berpengaruhnya. Disimpulkan bahwa integrasi kedua metode ini menyediakan kerangka kerja yang kuat untuk merancang strategi <em>Social</em> CRM yang lebih responsif dan berbasis data.</p><p><strong>Kata Kunci</strong><strong>:</strong> <em>Analisis Jaringan Sosial; Sentimen; Social CRM; Rekrutmen digital; Lexicon-Based Features.</em><em></em></p>

Candidate change agent identification among men at risk for HIV infection

Both the fields of Process Mining (PM) and Visual Analytics (VA) aim to make complex phenomena understandable. In PM, the goal is to gain insights into the execution of complex processes by analyzing the event data that is captured in event logs. This data is inherently multi-faceted, meaning that it covers various data facets, including spatial and temporal dependencies, relations between data entities (such as cases/events), and multivariate data attributes per entity. However, the multi-faceted nature of the data has not received much attention in PM. Conversely, VA research has investigated interactive visual methods for making multi-faceted data understandable for about two decades. In this study, we bring together PM and VA with the goal of advancing toward Visual Process Analytics (VPA) of multi-faceted processes. To this end, we present a systematic view of relevant (VA) data facets in the context of PM and assess to what extent existing PM visualizations address the data facets’ characteristics, making use of VA guidelines. In addition to visualizations, we look at how PM can benefit from analytical abstraction and interaction techniques known in the VA realm. Based on this, we discuss open challenges and opportunities for future research towards multi-faceted VPA.

Maintaining robustness is a key challenge for present and future air transportation. The analysis of network robustness is a time-demanding task, whose complexity increases with the size of networks. Accordingly, network attacks are often built on network metrics, for instance, attacking the nodes in decreasing order of their degree or betweenness. Albeit the results can be insightful, there is no guarantee regarding the quality or optimality of these attacks. In this paper, we propose a new exploration/exploitation search technique for a computationally efficient attacking model, adapted from general game playing. We propose an incremental solution for the efficient computation of robustness measures, by exploiting the network similarity before and after executing an attack, and thus, avoiding redundant computations. We define four tasks in the attacking model: Static attack, interactive attack, dynamic attack, and finding the best attack. The analysis of real-world air transportation networks reveals that commonly used network metric-based attacking strategies are already suboptimal for short attacks of length two. Our computationally efficient attacking model contributes to scalable analysis of robustness, not only for air transportation, but also for networks in general.

This paper introduces a constructive approach to online social networks analysis. We suggest a conceptual model of a social network, formulate major problems of analysis and control in social networks, describe methods and algorithms for activity analysis in social networks, as well as technologies for social networks monitoring and analysis.

Graph mining is a well-established research field, and lately it has drawn in considerable research communities. It allows to process, analyze, and discover significant knowledge from graph data. In graph mining, one of the most challenging tasks is frequent subgraph mining (FSM). FSM consists of applying the data mining algorithms to extract interesting, unexpected, and useful graph patterns from the graphs. FSM has been applied to many domains, such as graphical data management and knowledge discovery, social network analysis, bioinformatics, and security. In this context, a large number of techniques have been suggested to deal with the graph data. These techniques can be classed into two primary categories: (i) a priori-based FSM approaches and (ii) pattern growth-based FSM approaches. In both of these categories, an extensive research work is available. However, FSM approaches are facing some challenges, including enormous numbers of frequent subgraph patterns (FSPs); no suitable mechanism for applying ranking at the appropriate level during the discovery process of the FSPs; extraction of repetitive and duplicate FSPs; user involvement in supplying the support threshold value; large number of subgraph candidate generation. Thus, the aim of this research is to make do with the challenges of enormous FSPs, avoid duplicate discovery of FSPs, and use the ranking for such patterns. Therefore, to address these challenges a new FSM framework A RAnked Frequent pattern-growth Framework (A-RAFF) is suggested. Consequently, A-RAFF provides an efficacious answer to these challenges through the initiation of a new ranking measure called FSP-Rank. The proposed ranking measure FSP-Rank effectively reduced the duplicate and enormous frequent patterns. The effectiveness of the techniques proposed in this study is validated by extensive experimental analysis using different benchmark and synthetic graph datasets. Our experiments have consistently demonstrated the promising empirical results, thus confirming the superiority and practical feasibility of the proposed FSM framework.

There are various methods for handling longitudinal data in graphs and social networks, all of which have an impact on the algorithms used in data analysis. This article provides an overview of limitations, potential solutions, and unanswered questions regarding different temporal data schemas in social networks that are comparable to existing techniques. Restricting algorithms to a specific time point or layer has no effect on the results. However, when applying these approaches to a network with multiple time points, adjusted algorithms or reinterpretation becomes necessary. Therefore, using a generic definition of temporal networks as one graph, we aim to explore how we could analyze longitudinal social networks with centrality measures. Additionally, we introduce two new measures, “importance” and “change”, to identify nodes with specific behaviors. We provide case studies featuring three different real-world networks exhibiting both limitations and benefits of the novel approach. Furthermore, we present techniques to estimate variations in importance and degree centrality over time.

With the ever-increasing amount of data, the world has stepped into the era of “Big Data”. Presently, the analysis of massive and complex data and the extraction of relevant information, have been become essential tasks in many fields of studies, such as health, biology, chemistry, social science, astronomy, and physics. However, compared with the development of data storage and management technologies, our ability to gain useful information from the collected data does not match our ability to collect the data. This gap has led to a surge of research activity in the field of visual analytics. Visual analytics employs interactive visualization to integrate human judgment into algorithmic data-analysis processes. In this paper, the aim is to draw a complete picture of visual analytics to direct future research by examining the related research in various application domains. As such, a novel categorization of visual-analytics applications from a technical perspective is proposed, which is based on the dimensionality of visualization and the type of interaction. Based on this categorization, a comprehensive survey of visual analytics is performed, which examines its evolution from visualization and algorithmic data analysis, and investigates how it is applied in various application domains. In addition, based on the observations and findings gained in this survey, the trends, major challenges, and future directions of visual analytics are discussed.

BackgroundAs studies analyzing the networks and relational structures of research topics in academic fields emerge, studies that apply methods of network and relationship analysis, such as social network analysis (SNA), are drawing more attention. The purpose of this study is to explore the interaction of medical education subjects in the framework of complex systems theory using SNA and to analyze the trends in medical education.MethodsThe authors extracted keywords using Medical Subject Headings terms from 9,379 research articles (162,866 keywords) published in 1963–2015 in PubMed. They generated an occurrence frequency matrix, calculated relatedness using Weighted Jaccard Similarity, and analyzed and visualized the networks with Gephi software.ResultsNewly emerging topics by period units were identified as historical trends, and 20 global-level topic clusters were obtained through network analysis. A time-series analysis led to the definition of five historical periods: the waking phase (1963–1975), the birth phase (1976–1990), the growth phase (1991–1996), the maturity phase (1997–2005), and the expansion phase (2006–2015).ConclusionsThe study analyzed the trends in medical education research using SNA and analyzed their meaning using complex systems theory. During the 53-year period studied, medical education research has been subdivided and has expanded, improved, and changed along with shifts in society’s needs. By analyzing the trends in medical education using the conceptual framework of complex systems theory, the research team determined that medical education is forming a sense of the voluntary order within the field of medicine by interacting with social studies, philosophy, etc., and establishing legitimacy and originality.