Archambault D

Arleo A

Filipov V

von Landesberger T

Newcastle University E-Prints

Back to the Graphs: A Collection of Datasets and Quality Criteriafor Temporal Networks Layout and VisualizationVelitchko Filipov*TU WienVienna, AustriaAlessio Arleo*TU WienVienna, AustriaTatiana von Landesberger†University of CologneCologne, GermanyDaniel Archambault‡Newcastle UniversityNewcastle, UKABSTRACTDynamic network visualization is a rapidly evolving field and com-puting a node-link layout for these graphs is one of the most studiedproblems in this discipline. Benchmark datasets and quality criteriaare lacking in order to enable comparative evaluations of the layoutsproduced by different dynamic network visualization techniques.This paper proposes a collection of both discrete time and event-based dynamic quality criteria for evaluating dynamic graph layouts.Furthermore, we present and discuss datasets, generated from realdata, which we include in a benchmark set for future researchers touse in their evaluations.Index Terms: Human-centered computing—Visualization—Visualization techniques—Graphs;1 INTRODUCTIONNode-link representations of graphs on a plane are the most commonway to visualize graphs [16]. Typically, these graphs G = (V,E) en-code the vertices ν ∈V as circles linked together by edges e ∈ E, de-picted as lines. Dynamic network visualization develops approachesto depict the temporal dynamics of nodes and edges. Broadly speak-ing, time can be represented in two ways [2, 6]: mapping time tospace (i.e., small multiples) or time to time (i.e., animation).As the dynamic network visualization discipline evolves, differ-ent time abstractions are introduced. In graph drawing, time hasbeen modeled as snapshots of the graph at equally spaced, discretemoments in time (the timeslices) [6]. Recent techniques model thetemporal dynamics of nodes and edges (e.g., addition/deletion) asevents with time coordinates as real numbers and duration, hence thename temporal (or event-based) networks [10]. Modeling the graphdynamics in this fashion better approximates real-world phenomena,as data from microblogging messages, network communications,and contact tracing do not naturally fall neatly into timeslices. Thisnew perspective motivates this presentation of quality criteria andbenchmark datasets for designing and evaluating visualizations, anotherwise largely under-investigated topic.Our Contribution. We compile a list of quality criteria for event-based layouts; second, we survey and include real-world both times-liced and temporal graphs as benchmark datasets for further researchin the field. Our objective is to provide the community with a startingset of resources to support transparent, reproducible, and compara-tive evaluations of the techniques developed in this design space. Wealso advocate for more research in this direction given the advantagesof temporal networks in dynamic network visualization [4, 17].*e-mail: {velitchko.filipov—alessio.arleo@tuwien.ac.at}†e-mail: tlandesb@uni-koeln.de‡e-mail: daniel.archambault@newcastle.ac.uk2 RELATED WORKResearch on static graph quality metrics focuses on understandingwhich factors influence layout readability, e.g., through user studies(e.g., Purchase et al. [16]). From this work, it became clear that itwas not possible to optimize all quality criteria simultaneously, thusrequiring tradeoffs. Brandes et al. [7] conducted an experimentalstudy exploring the tradeoff between quality and stability in thecontext of timesliced graph drawing. Beck et al. [5] identified aset of aesthetic criteria for network animations. Among these, italso mentions the user mental map preservation, which has beeninvestigated in several studies [3]. As all related work focuses ontimesliced dynamic graphs and considering recent developments intemporal network visualization, this highlights gaps in literature andmotivates research on benchmark datasets and quality criteria.3 TEMPORAL NETWORKS: QUALITY CRITERIA & DATASETSQuality Criteria. We present quality criteria for drawing temporalnetworks that do not depend on the temporal encoding but are ratherindicators of the readability of the embedding in a 2D+ t space-timecube. These methods have been used in prior work [4, 17].◦ Node Movement tracks the average distance traveled by the nodesduring the evolution of the dynamic graph [17]. This metric isstrongly related to layout stability which is required to support thecognitive map [3] of the viewer. This metric has been used in bothtimesliced [7] and event-based [4,17] evaluations. Lower movementreflects stable graph drawings but some networks require, by theirnature, higher movement – for example when there is a fundamentalchange in a node’s local neighborhood in a short period of time.◦ Crowding counts the number of times a node passes close to an-other node during the evolution, meaning that node identities couldbe confused. It has been used in previous studies to evaluate draw-ings of temporal networks [4, 17]. Low crowding means that nodesare easily distinguishable and high crowding indicates that it couldbe hard to follow the node through the evolution of the dynamic net-work. High crowding may also result from low movement drawingswith the nodes very close to each other.◦ Stress measures the difference between the Euclidean and graphtheoretic distance in the drawing and has been extensively used toevaluate dynamic graph drawings [4, 7, 17]. Low stress drawingsplace nodes at Euclidean distances proportional to their distancein terms of the number of hops on the graph. In this context, weidentify two methods for computing stress: (i) Timesliced Stress,computed and averaged on a per timeslice basis, and (ii) ContinuousStress, calculated and averaged in continuous time, using the exactnode and edge appearances. In literature [4, 17], they are calledStressOn and StressOff respectively.Timesliced & Temporal Datasets. We present a collection oftimesliced and temporal networks that can be used for evaluatingvisualizations (see Table 1). The datasets have a variety of eventdistributions, matching the characteristics of several real-world sce-narios (e.g., alternating peaks and valleys, periods of stability, highvolatility). The datasets and code are openly available on GitHub [1].◦ Timesliced Graphs. Van Debunt: encodes the relationship between32 freshman at seven different time points [18]. Timeslices and edgesDataset Name Van Debunt Newcomb InfoVis Rugby Pride MOOC RM MSG RAMPReference [18] [14] [11] [17] [9] [12] [8] [15] [13]Dataset Type Timesliced Timesliced Timesliced Event-based Event-based Event-based Event-based Event-based Event-based# of Events 0.1k 0.6k 2.8k 3.1k 4.0k 15k 28k 15k 0.8kTrendsTable 1: Overview of timesliced and event-based datasets with their references. The trends represent the “shape” of changes, or event distribution(i.e., % of total number of events over time) for each graph dataset. Each graph has a different temporal extent but the same Y-axis.are selected as in the paper by Brandes and Mader [7]. Newcomb:contains the sociometric preference of 17 members of a fraternityin the University of Michigan in the fall of 1956 [14]. Timeslicesare selected as in previous work [7]. InfoVis: is a co-authorshipnetwork for papers published in the InfoVis conference from 1995to 2015 [11]. Authors of a paper are connected in a clique at thetime of publication. This is not a cumulative network as authors canappear, disappear and appear again. The dataset has 21 timeslices(one per year). Although timesliced, previous studies have shownthat event-based drawings of this data [4, 17] outperform timeslicedbased methods due to the drastic changes between timeslices.◦ Temporal Networks. Rugby is derived from over 3000 tweetsinvolving teams in the ‘Guinness Pro12’ rugby competition. Thetweets were posted between 1 September 2014 and 23 October 2015.Each edge has a time of publication with a precision down to thesecond. Pride: lists the dialogues between characters in the novel‘Pride and Prejudice’ in order [9]. The book has 61 chapters with4000 interactions between characters. When the algorithm requiredtimeslices as input, we divided this data into 61 of them (one foreach chapter). The graph can naturally be considered a temporalnetwork by simply considering the character interactions in order.MOOC: represents the actions taken by users on a popular massiveopen online class platform [12]. The nodes represent users andcourse activities (targets), and the edges represent the actions byusers. We pick and elaborate the first 15 thousand events. RM: isthe data coming from The Reality Mining study [8]. It followed 94participants using mobile phones pre-installed with several pieces ofsoftware that recorded their actions. We only consider voice callsand consider the first 28 thousand events. MSG: is comprised ofprivate messages sent on an online social network at the Universityof California, Irvine [15]. Each message is represented as a temporaledge. We consider the first 15 thousand events. RAMP: consists ofthe simulated spread of COVID-19. It was developed by the ScottishCOVID-19 Response Consortium contact tracing model [13]. Nodesare infected individuals and edges occur on the day when the illnesswas transmitted. We consider the first 800 events.4 CONCLUSIONThis paper presents a collection of datasets and quality criteria forsupporting further research on the visualization and layout of tempo-ral networks. Event-based layout showed higher quality drawingson temporal graphs, due to all temporal information being used, andnot (partially) lost in discretization. We believe that these resourcescan benefit both researchers and practitioners pursuing this researchdirection, by enabling them to develop, compare, and evaluate tech-niques. Future work can extend this collection of temporal networkmetrics and datasets by incorporating more graphs and expanding thequality criteria to cover additional aspects of network visualization,such as user interaction, engagement, and perception.ACKNOWLEDGEMENTSFor the purpose of open access, the author has applied a CreativeCommons Attribution (CC-BY) license to any Author AcceptedManuscript version arising from this submission. This work is sup-ported by the Austrian Science Fund (FWF) grant ArtVis [P35767].REFERENCES[1] Multidynnos github repository. https://github.com/EngAAlex/MultiDynNos/. Online, accessed 2023-08-03.[2] W. Aigner, S. Miksch, H. Schumann, and C. Tominski. Visualizationof Time-Oriented Data. Second edition ed., 2023. doi: Forthcoming[3] D. Archambault and H. C. Purchase. 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Back to the Graphs: A Collection of Datasets and Quality Criteria for Temporal Networks Layout and Visualization

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