research-article

EgoNav: exploring networks through egocentric spatializations

Authors:

Martin Harrigan,

Daniel Archambault,

Pádraig Cunningham,

Neil HurleyAuthors Info & Claims

AVI '12: Proceedings of the International Working Conference on Advanced Visual Interfaces

Pages 563 - 570

https://doi.org/10.1145/2254556.2254661

Published: 21 May 2012 Publication History

Abstract

EgoNav is a visual analytics system that characterizes egos based on the relationship structure of their egocentric networks and presents the results as a spatialization. An ego, or individual node in a network, is most closely related to its neighbors, and to a lesser degree, to its neighbor's neighbors. For example, in social networks, people are closely related to their friends and family. In financial networks, the affairs of borrowers and lenders are more closely tied to each other. In fact, the relationship structure surrounding an ego, or an egocentric network, can provide characteristic information about the ego itself. Using network motif analysis and dimensionality reduction techniques, the system places egos in similar areas of a spatialization if their egocentric networks are structurally similar. This view of a network discriminates between the various classes of typical and exceptional egos. We demonstrate its effectiveness using appropriate synthetic datasets, real-world mobile phone call and peer-to-peer lending datasets. We subsequently elicit user feedback from experts involved in the investigation of financial fraud to assess the tool's applicability in this domain.

References

[1]

L. Antiqueira and L. da Fontoura Costa. Characterization of Subgraph Relationships and Distribution in Complex Networks. New Journal of Physics, 11(013058), 2009.

[2]

A. Bezerianos, F. Chevalier, P. Dragicevic, N. Elmqvist, and J. Fekete. GraphDice: A System for Exploring Multivariate Social Networks. Computer Graphics Forum, 29(3):863--872, 2010.

Digital Library

[3]

V. Blondel, A. Gajardo, M. Heymans, P. Senellart, and P. Van Dooren. A Measure of Similarity between Graph Vertices. SIAM Review, 46(4):647--666, 2004.

Digital Library

[4]

S. Borgatti and M. Everett. The Class of All Regular Equivalences: Algebraic Structure and Computation. Social Networks, 11:65--88, 1989.

[5]

U. Brandes, J. Lerner, M. Lubbers, C. McCarty, and J. Molina. Visual Statistics for Collections of Clustered Graphs. In Proc. of the IEEE VGTC Pacific Visualization Symp. (PacificVis'08), pages 47--54, 2008.

[6]

U. Brandes, J. Lerner, U. Nagel, and B. Nick. Structural Trends in Network Ensembles. In Proc. of the 1^st Int'l. Workshop on Complex Networks (CompleNet'09), pages 83--97, 2009.

[7]

N. Eagle, A. Pentland, and D. Lazer. Inferring Friendship Network Structure by Using Mobile Phone Data. PNAS, 106(36):15274--15278, 2009.

[8]

P. Erdös and A. Rényi. On Random Graphs. Publicationes Mathematicae Debrecen, 6:290--297, 1959.

[9]

M. Freire, C. Plaisant, B. Shneiderman, and J. Golbeck. ManyNets: An Interface for Multiple Network Analysis and Visualization. In Proc. of the ACM Conf. on Human Factors in Computing Systems (CHI'10), pages 213--222, 2010.

Digital Library

[10]

R. Ginoza and A. Mugler. Network Motifs Come in Sets: Correlations in the Randomization Process. Physical Review E, 82(1), 2010.

[11]

R. Giugno and D. Shasha. GraphGrep: A Fast and Universal Method for Querying Graphs. In Proc. of the 16^th Int'l. Conf. on Pattern Recognition (ICPR'02), pages 112--115, 2002.

[12]

W. Huang, C. Murray, X. Shen, L. Song, Y. Xin Wu, and L. Zheng. Visualisation and Analysis of Network Motifs. In Proc. of the 9^th Int'l. Conf. on Information Visualisation (IV'05), pages 697--702, 2005.

Digital Library

[13]

D. Jeong, C. Ziemkiewicz, B. Fisher, W. Ribarsky, and R. Chang. iPCA: An Interactive System for PCA-Base Visual Analytics. In Proc. of the 11^th Eurographics/IEEE Symp. on Visualization (EuroVis'09), pages 767--774, 2009.

Digital Library

[14]

R. Khan, M. Corney, A. Clark, and G. Mohay. Transaction Mining for Fraud Detection in ERP Systems. Industrial Engineering & Management Systems, 9(2):141--156, 2010.

[15]

C. Klukas, F. Schreiber, and H. Schwöbbermeyer. Coordinated Perspectives and Enhanced Force-Directed Layout for the Analysis of Network Motifs. In Proc. of the 5^th Asia-Pacific Symp. on Visualization (APVIS'06), pages 39--48, 2006.

Digital Library

[16]

D. Koschützki, H. Schwöbbermeyer, and F. Schreiber. Ranking of Network Elements Based on Functional Substructures. Journal of Theoretical Biology, 248(3):471--479, 2007.

[17]

E. Leicht, P. Holme, and M. Newman. Vertex Similarity in Networks. Physical Review E, 73(026120), 2006.

[18]

C. Li and S. Lin. Egocentric Information Abstraction for Heterogeneous Social Networks. In Proc. of the 1^st Int'l. Conf. on Social Networks Analysis & Mining (ASONAM'09), pages 255--260, 2009.

Digital Library

[19]

F. Lorrain and H. White. Structural Equivalence of Individuals in Social Networks. Journal of Mathematical Sociology, 1:49--80, 1971.

[20]

M. Lubbers, J. Molina, J. Lerner, U. Brandes, J. Ávila, and C. McCarty. Longitudinal Analysis of Personal Networks: The Case of Argentinean Migrants in Spain. Social Networks, 32(1):91--104, 2010.

[21]

A. Ma'ayan, S. Jenkins, R. Webb, S. Berger, S. Purushothaman, N. Abul-Husn, J. Posner, T. Flores, and R. Iyengar. SNAVI: Desktop Application for Analysis and Visualization of Large-Scale Signaling Networks. BMC Systems Biology, 3(10), 2009.

[22]

B. McKay. Isomorph-Free Exhaustive Generation. Journal of Algorithms, 26(2):306--324, 1998.

Digital Library

[23]

F. McSherry. Spectral Partitioning of Random Graphs. In Proc. of the 42^nd Annual Symp. on Foundations of Computer Science (FOCS'01), pages 529--537, 2001.

Digital Library

[24]

R. Milo, S. Itzkovitz, N. Kashtan, R. Levitt, S. Shen-Orr, I. Ayzenshtat, M. Sheffer, and U. Alon. Superfamilies of Evolved and Designed Networks. Science, 303(5663):1538--1542, 2004.

[25]

R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon. Network Motifs: Simple Building Blocks of Complex Networks. Science, 298(5594):824--827, 2002.

[26]

S. Roweis and L. Saul. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 22(5500):2323--2326, 2000.

[27]

F. Schreiber and H. Schwöbbermeyer. MAVisto: A Tool for the Exploration of Network Motifs. Bioinformatics, 21(17):3572--3574, 2005.

Digital Library

[28]

A. Stoica and C. Prieur. Structure of Neighborhoods in a Large Social network. In Proc. of the Int'l. Conf. on Computational Science & Engineering (CSE'09), pages 26--33, 2009.

Digital Library

[29]

J. Tenenbaum, V. de Silva, and J. Langford. A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science, 22(290):2319--2323, 2000.

[30]

M. Tory, D. Sprague, F. Wu, W. So, and T. Munzner. Spatialization Design: Comparing Points and Landscapes. IEEE Trans. on Visualization & Computer Graphics (TVCG), 13(6):1262--1269, 2007.

Digital Library

[31]

T. von Landesberger, M. Görner, and T. Schreck. Visual Analysis of Graphs with Multiple Connected Components. In Proc. of the IEEE Symp. on Visual Analytics Science & Technology (VAST'09), pages 155--162, 2009.

[32]

D. Watts and S. Strogatz. Collective Dynamics of 'Small-World' Networks. Nature, 393:440--442, 1998.

[33]

B. Wellman. An Egocentric Network Tale: Comment on Bien et al. Social Networks, 15:423--436, 1993.

[34]

H. Welser, E. Gleave, D. Fisher, and M. Smith. Visualizing the Signatures of Social Roles in Online Discussion Groups. Journal of Social Structure, 8, 2007.

[35]

S. Wernicke and F. Rasche. FANMOD: A Tool for Fast Network Motif Detection. Bioinformatics, 22(9):1152--1153, 2006.

Digital Library

[36]

H. White, S. Boorman, and R. Breiger. Social Structure from Multiple Networks -- Blockmodels of Roles and Positions. American Journal of Sociology, 81:730--780, 1976.

Cited By

Lu HFujiwara TChang MFu YYnnerman AMa K(2023)Visual analytics of multivariate networks with representation learning and composite variable constructionVisual Informatics10.1016/j.visinf.2023.06.004Online publication date: Jun-2023
https://doi.org/10.1016/j.visinf.2023.06.004
Sondag MTurkay CXu KMatthews LMohr SArchambault D(2022)Visual Analytics of Contact Tracing Policy Simulations During an Emergency ResponseComputer Graphics Forum10.1111/cgf.1452041:3(29-41)Online publication date: 29-Jul-2022
https://doi.org/10.1111/cgf.14520
Cakmak EFuchs JJackle DSchreck TBrandes UKeim D(2022)Motif-Based Visual Analysis of Dynamic Networks2022 IEEE Visualization in Data Science (VDS)10.1109/VDS57266.2022.00007(17-26)Online publication date: Oct-2022
https://doi.org/10.1109/VDS57266.2022.00007
Show More Cited By

Index Terms

EgoNav: exploring networks through egocentric spatializations
1. Human-centered computing
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms

Recommendations

PerSoN-Vis: Visualizing Personal Social Networks (Ego Networks)
CHI EA '16: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems

Recently, we witness a high usage of social media platforms for different purposes, from contacting with friends and colleagues to raising voice for different causes. Many times, on such social media platforms people are also interested to see and ...
SocialNetSense: Supporting sensemaking of social and structural features in networks with interactive visualization
VAST '12: Proceedings of the 2012 IEEE Conference on Visual Analytics Science and Technology (VAST)

Increasingly, social network datasets contain social attribute information about actors and their relationship. Analyzing such network with social attributes requires making sense of not only its structural features, but also the relationship between ...
Analysis of terrorist social networks with fractal views

Social network visualization has drawn significant attention over recent years. It creates images of social networks that provide investigators with new insights about network structures and helps them to communicate those insights to others. ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

AVI '12: Proceedings of the International Working Conference on Advanced Visual Interfaces

May 2012

846 pages

ISBN:9781450312875

DOI:10.1145/2254556

Editors:
Genny Tortora
Università di Salerno, Italy
,
Stefano Levialdi
Sapienza Università di Roma, Italy
,
Maurizio Tucci
Università di Salerno, Italy

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Consulta Umbria SRL
University of Salerno: University of Salerno

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 May 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

AVI'12

Sponsor:

University of Salerno

AVI'12: International Working Conference on Advanced Visual Interfaces

May 21 - 25, 2012

Capri Island, Italy

Acceptance Rates

Overall Acceptance Rate 128 of 490 submissions, 26%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

14
Total Citations
View Citations
254
Total Downloads

Downloads (Last 12 months)14
Downloads (Last 6 weeks)1

Reflects downloads up to 28 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Lu HFujiwara TChang MFu YYnnerman AMa K(2023)Visual analytics of multivariate networks with representation learning and composite variable constructionVisual Informatics10.1016/j.visinf.2023.06.004Online publication date: Jun-2023
https://doi.org/10.1016/j.visinf.2023.06.004
Sondag MTurkay CXu KMatthews LMohr SArchambault D(2022)Visual Analytics of Contact Tracing Policy Simulations During an Emergency ResponseComputer Graphics Forum10.1111/cgf.1452041:3(29-41)Online publication date: 29-Jul-2022
https://doi.org/10.1111/cgf.14520
Cakmak EFuchs JJackle DSchreck TBrandes UKeim D(2022)Motif-Based Visual Analysis of Dynamic Networks2022 IEEE Visualization in Data Science (VDS)10.1109/VDS57266.2022.00007(17-26)Online publication date: Oct-2022
https://doi.org/10.1109/VDS57266.2022.00007
Isike C(2022)Foes, Friends or Both? Looking Beyond Hostility in Relations Between Congolese Migrants and South Africans in EmpangeniConflict and Concord10.1007/978-981-19-1033-3_9(157-178)Online publication date: 11-May-2022
https://doi.org/10.1007/978-981-19-1033-3_9
Gove R(2022)Gragnostics: Evaluating Fast, Interpretable Structural Graph Features for Classification and Visual AnalyticsIntegrating Artificial Intelligence and Visualization for Visual Knowledge Discovery10.1007/978-3-030-93119-3_12(311-336)Online publication date: 5-Jun-2022
https://doi.org/10.1007/978-3-030-93119-3_12
Fujiwara TZhao JChen FMa K(2020)A Visual Analytics Framework for Contrastive Network Analysis2020 IEEE Conference on Visual Analytics Science and Technology (VAST)10.1109/VAST50239.2020.00010(48-59)Online publication date: Oct-2020
https://doi.org/10.1109/VAST50239.2020.00010
Gove R(2019)Gragnostics: Fast, Interpretable Features for Comparing Graphs2019 23rd International Conference Information Visualisation (IV)10.1109/IV.2019.00042(201-209)Online publication date: Jul-2019
https://doi.org/10.1109/IV.2019.00042
Charbey RPrieur C(2019)Stars, holes, or paths across your Facebook friends: A graphlet-based characterization of many networksNetwork Science10.1017/nws.2019.207:4(476-497)Online publication date: 19-Sep-2019
https://doi.org/10.1017/nws.2019.20
Scaria APhilip RWest RLeskovec JCarterette BDiaz FCastillo CMetzler D(2014)The last clickProceedings of the 7th ACM international conference on Web search and data mining10.1145/2556195.2556232(213-222)Online publication date: 24-Feb-2014
https://dl.acm.org/doi/10.1145/2556195.2556232
Archambault DHurley N(2014)Visualization of trends in subscriber attributes of communities on mobile telecommunications networksSocial Network Analysis and Mining10.1007/s13278-014-0205-54:1Online publication date: 8-Jun-2014
https://doi.org/10.1007/s13278-014-0205-5
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents