DOCSLIB.ORG

Classifying Types of Network Communities Using Motifs

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Classifying Types of Network Communities Using Motifs Classifying Types of Network Communities Using Motifs Kun Tu1, Jian Li1, Don Towsley1, Dave Braines2, Liam Turner3 1University of Massachusetts Amherst, 2IBM UK, 3Cardiff University Machine Learning, Data Analytics and Modeling September 26, 2018, Thessaloniki, Greece Network Community Classification • Detect type of relations within groups of individuals • Help to understand patterns of interaction between people • Application in identifying • Department ID in email networks • User ID from mobile app switching behavior represented as a network Contribution Propose a new network embedding (gl2vec) methodology for network classification in directed networks based on a network’s network motif counts Problem Formulation ) • !" #", %", &" "'(: set of * (sub)graphs/networks • #": set of nodes • %": set of timestamped edges • &" ∈ {1, … , /}: class label of !" Problem Formulation ) • !" #", %", &" "'(: set of * (sub)graphs/networks • #": set of nodes • %": set of timestamped edges • &" ∈ {1, … , /}: class label of !" 5 • 1: !" → 4 : graph embedding function from Gi to 1*m feature representation vector using subgraph ratio profile (SRP) of static motif Problem Formulation ) • !" #", %", &" "'(: set of * (sub)graphs/networks • #": set of nodes • %": set of timestamped edges • &" ∈ {1, … , /}: class label of !" 5 • 1: !" → 4 : graph embedding function from Gi to 1*m feature representation vector using subgraph ratio profile (SRP) of static motif • 6: 45 → 7: classifier mapping feature vector to a categorical distribution 7 = [:(, … , :;] for / labels Problem Formulation ) • !" #", %", &" "'(: set of * (sub)graphs/networks • #": set of nodes • %": set of timestamped edges • &" ∈ {1, … , /}: class label of !" 5 • 1: !" → 4 : graph embedding function from Gi to 1*m feature representation vector using subgraph ratio profile (SRP) of static motif • 6: 45 → 7: classifier mapping feature vector to a categorical distribution 7 = [:(, … , :;] for / labels • Goal: design an embedding function f and select a machine learning model g to minimize the sum of cross entropy for all graphs Network Motifs • Static motifs: triad - three node motifs Subgraph Ratio Profile (SRP) • SRP for motif ! • "#$%&: average counts of motif ! in random graphs • "(): observed counts of ! in empirical graph -./01-23456 • Δ+ = -23457 1-2345678 • 9: keeps Δ+ not too large when motif ! is rare Δ+ :;<+ = > ∑Δ+ Subgraph Ratio Profile (SRP) • SRP for motif ! • "#$%&: average counts of motif ! in random graphs • "(): observed counts of ! in empirical graph -./01-23456 • Δ+ = -23457 1-2345678 • 9: keeps Δ+ not too large when motif ! is rare Δ+ :;<+ = > ∑Δ+ • SRP can be used to compare networks of different sizes since an SRP for a motif is a normalized term Null Model for Networks • null model • a generative model that generates random graphs that matches a specific graph in some of its structural features Null Model for Networks • null model • generates random graphs with same structural features as a specific graph • Static network null model • NE: # nodes and # edges (fast) • MAN: # (M)utual, (A)symetric and (N)ull edges • BDS: In/out degree sequence (slow) Experiments • Classification problems: given network topological structure • Network domain classification • 15 network domains, including email network, QA network, p2p, … • Subgraph identification • Department ID in email network • User ID given mobile app switching behaviors as a network • Baseline embedding methods • Struc2vec • Graph Kernel with graphlet (GK graphlet) • Graph kernel with Weisfeiler-lehman test of isomorphism (GK WL) • Node2vec • Graph2vec • Sub2vec • Motif distribution as feature vector Real-world Data • 2,355 real-world networks in 15 network domains (from SNAP, Tymer project): • Social network: Google+, Twitter • Email network: EmailEU, BBNEmailTraffic, Enron • QA network: askubuntu, mathoverflow • P2p: Gnutella • Citation: HepHh, HepTh (physics paper) • Friendship: slashdot • Others: Bitcoin, co-sponsorship, SwitchApp42, Epinion, WikiVote, Advice, terrorist • More than 10,000 network communities obtained • From ground truth information • From community detection algorithm (Newman’s modularity) Experiment Setup • Machine learning classifier • XGBoost: better than most classifiers for mid-size datasets • SVM: good for small dataset • Random forest: feature selection to investigate our network embedding • Ada boost: baseline • Parameter setting: • Grid search for best performance • XGBoost: learning rate is 0.1, maximal tree depth is 8, minimal child weight is 1 and the subsample ratio of train instances is 0.8 • SVM: regularization weight in is 2 • Random forest: # tree is 400 and the minimal samples required to split a tree node is 2 Community Domain • gl2vec is competitive • gl2vec with NE is slightly better • Combine gl2vec embedding with other methods significantly improve accuracy • XGBoost performs best EmailEU Departments • gl2vec is competitive • gl2vec with NE is slightly better • Combine gl2vec embedding with other methods significantly improve accuracy • Random forest performs best BBN Department • gl2vec is competitive • gl2vec with NE is slightly better • Combine gl2vec embedding with other methods significantly improve accuracy Mobile Users • gl2vec is competitive • gl2vec with NE is slightly better • Combine gl2vec embedding with other methods significantly improve accuracy Conclusion • gl2vec is competitive with state-of-the-art methods • Combination of gl2vec and state-of-the-art methods yield significant improvement • Subgraph ratio profiles (SRP) contain important features for network classification • All three null model have similar prediction accuracy but NE is recommended because of simple computational complexity Thank you!.
Load more

Recommended publications
  • A Novel Neuronal Network Approach to Express Network Motifs
    Introduction to NeuraBASE: A Novel Neuronal Network Approach to Express Network Motifs Robert Hercus Choong-Ming Chin Kim-Fong Ho Introduction Since the discovery by Alon et al. [1-3], network motifs are now at the forefront of unravelling complex networks, ranging from biological issues to managing the traffic on the Internet. Network motifs are used, in general, as a technique to detect recurring patterns featured in networks. This is based on the assumption that information flows in distinct networks and, hence, common causal associations between nodal networks can be surmised statistically. In essence, the motif discovery algorithm [1-4] begins with a list of variable-information of a particular network and, their connections to other network variables. An analysis is subsequently made on the most prevalent causal relationships in the network based on the frequency of occurrences. These are then compared with a randomised network with the same number of variables and directed edges. Finally, the algorithm lists out statistically significant patterns of associations that occur more frequently than they would at random. In the paper by Alon et al. [2], it was reported that most of the networks analysed demonstrate identical motifs, even though they belong to disparate network families. For example, the said literature discovered that gene regulation in genetics, neuronal connectivity network and electronic circuit systems share two identical network motifs, which involved less than four variables. This observation implies that some similarities exist in these three network architectures. Besides the MFinder tool [1], researchers have also developed other network motif discovery tools. However, MAVisto [6] was found to be as computationally costly with long run-time periods as the MFinder tool uses the same network search for the same subgraph sizes.
    [Show full text]
  • A Meta-Analysis of Boolean Network Models Reveals Design Principles of Gene Regulatory Networks
    A meta-analysis of Boolean network models reveals design principles of gene regulatory networks Claus Kadelkaa,∗, Taras-Michael Butrieb,1, Evan Hiltonc,1, Jack Kinsetha,1, Haris Serdarevica,1 aDepartment of Mathematics, Iowa State University, Ames, Iowa 50011 bDepartment of Aerospace Engineering, Iowa State University, Ames, Iowa 50011 cDepartment of Computer Science, Iowa State University, Ames, Iowa 50011 Abstract Gene regulatory networks (GRNs) describe how a collection of genes governs the processes within a cell. Understanding how GRNs manage to consistently perform a particular function constitutes a key question in cell biology. GRNs are frequently modeled as Boolean networks, which are intuitive, simple to describe, and can yield qualitative results even when data is sparse. We generate an expandable database of published, expert-curated Boolean GRN models, and extracted the rules governing these networks. A meta-analysis of this diverse set of models enables us to identify fundamental design principles of GRNs. The biological term canalization reflects a cell's ability to maintain a stable phenotype despite ongoing environmental perturbations. Accordingly, Boolean canalizing functions are functions where the output is already determined if a specific variable takes on its canalizing input, regardless of all other inputs. We provide a detailed analysis of the prevalence of canalization and show that most rules describing the regulatory logic are highly canalizing. Independent from this, we also find that most rules exhibit a high level of redundancy. An analysis of the prevalence of small network motifs, e.g. feed-forward loops or feedback loops, in the wiring diagram of the identified models reveals several highly abundant types of motifs, as well as a surprisingly high overabundance of negative regulations in complex feedback loops.
    [Show full text]
  • Multivariate Relations Aggregation Learning in Social Networks
    Multivariate Relations Aggregation Learning in Social Networks Jin Xu1, Shuo Yu1, Ke Sun1, Jing Ren1, Ivan Lee2, Shirui Pan3, Feng Xia4 1 School of Software, Dalian University of Technology, Dalian, China 2 School of IT and Mathematical Sciences, University of South Australia, Adelaide, Australia 3 Faculty of Information Technology, Monash University, Melbourne, Australia 4 School of Science, Engineering and Information Technology, Federation University Australia, Ballarat, Australia [email protected],[email protected],[email protected],[email protected] [email protected],[email protected],[email protected] ABSTRACT 1 INTRODUCTION Multivariate relations are general in various types of networks, such We are living in a world full of relations. It is of great significance as biological networks, social networks, transportation networks, to explore existing social relationships as well as predict poten- and academic networks. Due to the principle of ternary closures tial relationships. These abundant relations generally exist among and the trend of group formation, the multivariate relationships in multiple entities; thus, these relations are also called multivariate social networks are complex and rich. Therefore, in graph learn- relations. Multivariate relations are the most fundamental rela- ing tasks of social networks, the identification and utilization of tions, containing interpersonal relations, public relations, logical multivariate relationship information are more important. Existing relations, social relations, etc [3, 37]. Multivariate relations are of graph learning methods are based on the neighborhood informa- more complicated structures comparing with binary relations. Such tion diffusion mechanism, which often leads to partial omission or higher-order structures contain more information to express inner even lack of multivariate relationship information, and ultimately relations among multiple entities.
    [Show full text]
  • Motif Aware Node Representation Learning for Heterogeneous Networks
    motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks Manoj Reddy Dareddy* Mahashweta Das Hao Yang University of California, Los Angeles Visa Research Visa Research Los Angeles, CA, USA Palo Alto, CA, USA Palo Alto, CA, USA [email protected] [email protected] [email protected] Abstract—Recent years have witnessed a surge of interest in is originally sequential in nature [35], product review graph machine learning on graphs and networks with applications constructed from reviews written by users for stores [34], ranging from vehicular network design to IoT traffic manage- credit card fraud network constructed from fraudulent and non- ment to social network recommendations. Supervised machine learning tasks in networks such as node classification and link fraudulent transaction activity data [33], etc. prediction require us to perform feature engineering that is Supervised machine learning tasks over nodes and links known and agreed to be the key to success in applied machine in networks1 such as node classification and link prediction learning. Research efforts dedicated to representation learning, require us to perform feature engineering that is known and especially representation learning using deep learning, has shown agreed to be the key to success in applied machine learning. us ways to automatically learn relevant features from vast amounts of potentially noisy, raw data. However, most of the However, feature engineering is challenging and tedious since methods are not adequate to handle heterogeneous information
    [Show full text]
  • GAMARALLAGE-THESIS-2020.Pdf
    c 2020 Anuththari Gamarallage NETWORK MOTIF PREDICTION USING GENERATIVE MODELS FOR GRAPHS BY ANUTHTHARI GAMARALLAGE THESIS Submitted in partial fulfillment of the requirements for the degree of Master of Science in Electrical and Computer Engineering in the Graduate College of the University of Illinois at Urbana-Champaign, 2020 Urbana, Illinois Adviser: Professor Olgica Milenkovic ABSTRACT Graphs are commonly used to represent pairwise interactions between dif- ferent entities in networks. Generative graph models create new graphs that mimic the properties of already existing graphs. Generative models are suc- cessful at retaining the pairwise interactions of the underlying networks but often fail to capture higher-order connectivity patterns between more than two entities. A network motif is one such pattern observed in various real- world networks. Different types of graphs contain different network motifs, an example of which are triangles that often arise in social and biological net- works. Motifs model important functional properties of the graph. Hence, it is vital to capture these higher-order structures to simulate real-world net- works accurately. This thesis introduces a motif-targeted graph generative model based on a generative adversarial network (GAN) architecture that generalizes and outperforms the current benchmark approach, NetGAN, at motif prediction. This model and its extension to hypergraphs are tested on real-world social and biological network data, and they are shown to be better at both capturing the underlying motif statistics in the networks as well as predicting missing motifs in incomplete networks. ii ACKNOWLEDGMENTS The work was supported by the NSF Center for Science of Information under grant number 0939370.
    [Show full text]
  • Motifs in Temporal Networks
    Motifs in Temporal Networks ∗ ∗ Ashwin Paranjape Austin R. Benson Jure Leskovec Stanford University Stanford University Stanford University [email protected] [email protected] [email protected] ABSTRACT c A b B 15s, 32s 3 Networks are a fundamental tool for modeling complex systems in 1 2 31s a variety of domains including social and communication networks 25s 17s, 28s, 30s, 35s as well as biology and neuroscience. Small subgraph patterns in networks, called network motifs, are crucial to understanding the a 14s d structure and function of these systems. However, the role of network motifs in temporal networks, which contain many times- C b c b c d a d c d c tamped links between the nodes, is not yet well understood. 32s 32s 35s 15s 32s Here we develop a notion of a temporal network motif as an ele- 25s 28s 25s 30s 31s 32s 14s 17s 14s 28s mentary unit of temporal networks and provide a general method- a a c a a ology for counting such motifs. We define temporal network motifs Figure 1: Temporal graphs and δ-temporal motifs. A: A tempo- as induced subgraphs on sequences of temporal edges, design fast ral graph with nine temporal edges. Each edge has a timestamp algorithms for counting temporal motifs, and prove their runtime (listed here in seconds). B: Example 3-node, 3-edge δ-temporal complexity. Our fast algorithms achieve up to 56.5x speedup com- motif M. The edge labels correspond to the ordering of the pared to a baseline method. Furthermore, we use our algorithms to edges.
    [Show full text]
  • Biological Network Motif Detection: Principles and Practice Elisabeth Wong, Brittany Baur, Saad Quader and Chun-Hsi Huang
    Briefings in Bioinformatics Advance Access published June 20, 2011 BRIEFINGS IN BIOINFORMATICS. page1of14 doi:10.1093/bib/bbr033 Biological network motif detection: principles and practice Elisabeth Wong, Brittany Baur, Saad Quader and Chun-Hsi Huang Submitted: 28th February 2011; Received (in revised form): 13th May 2011 Abstract Network motifs are statistically overrepresented sub-structures (sub-graphs) in a network, and have been recog- nized as ‘the simple building blocks of complex networks’. Study of biological network motifs may reveal answers to many important biological questions. The main difficulty in detecting larger network motifs in biological networks lies in the facts that the number of possible sub-graphs increases exponentially with the network or motif size Downloaded from (node counts, in general), and that no known polynomial-time algorithm exists in deciding if two graphs are topo- logically equivalent. This article discusses the biological significance of network motifs, the motivation behind solving the motif-finding problem, and strategies to solve the various aspects of this problem. A simple classification scheme is designed to analyze the strengths and weaknesses of several existing algorithms. Experimental results derived from a few comparative studies in the literature are discussed, with conclusions that lead to future research http://bib.oxfordjournals.org/ directions. Keywords: Network motifs; biological networks; graph isomorphism INTRODUCTION motifs are also studied in such other networks as the Biology of a living cell involves many intricate electronic circuits and power distribution networks, networks of interdependent events and interactions ecological networks (food web), software engineer- at Weizmann Institute of Science on May 31, 2012 among biomolecules.
    [Show full text]
  • Subgraphs and Motifs in a Dynamic Airline Network Marius Agasse-Duval, Steve Lawford
    Subgraphs and Motifs in a Dynamic Airline Network Marius Agasse-Duval, Steve Lawford To cite this version: Marius Agasse-Duval, Steve Lawford. Subgraphs and Motifs in a Dynamic Airline Network. 2019. hal-02017122 HAL Id: hal-02017122 https://hal-enac.archives-ouvertes.fr/hal-02017122 Preprint submitted on 13 Feb 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Subgraphs and Motifs in a Dynamic Airline Network∗ Marius Agasse-Duval and Steve Lawford ENAC, University of Toulouse Abstract How does the small-scale topological structure of an airline network behave as the network evolves? To address this question, we study the dynamic and spatial properties of small undirected subgraphs using 15 years of data on Southwest Airlines’ domestic route service. We find that this real-world network has much in common with random graphs, and describe a possible power-law scaling between subgraph counts and the number of edges in the network, that appears to be quite robust to changes in network density and size. We use analytic formulae to identify statistically over- and under- represented subgraphs, known as motifs and anti-motifs, and discover the existence of substantial topology transitions.
    [Show full text]
  • Efficient and Scalable Algorithms for Network Motifs Discovery
    Pedro Manuel Pinto Ribeiro Efficient and Scalable Algorithms for Network Motifs Discovery Departamento de Ciˆencia de Computadores Faculdade de Ciˆencias da Universidade do Porto 2011 Pedro Manuel Pinto Ribeiro Efficient and Scalable Algorithms for Network Motifs Discovery Tese submetida `aFaculdade de Ciˆencias da Universidade do Porto para obten¸c˜ao do grau de Doutor em Ciˆencia de Computadores Advisors: Prof. Fernando Silva and Prof. Lu´ıs Lopes Departamento de Ciˆencia de Computadores Faculdade de Ciˆencias da Universidade do Porto Junho de 2011 Thesis Committee: Prof. Ant´onio Porto, University of Porto (Chair) Prof. Arlindo Oliveira, IST - Technical University of Lisbon (Examiner) Prof. Marcus Kaiser, Newcastle University (Examiner) Prof. Jos´eCardoso e Cunha, New University of Lisbon Prof. V´ıtor Santos Costa, University of Porto Prof. Fernando Silva, University of Porto (Advisor) Prof. Lu´ıs Lopes, University of Porto (Co-Advisor) 2 To Samuel and Liliana 3 4 Acknowledgments First of all, I would like to thank my two advisors, Fernando Silva and Lu´ıs Lopes. They have always been there when I needed them, providing guidance, support and a lot of patience over the last years. Their collaboration was essential and we always had fruitful discussions on the emerging ideas on this subject. I would like to thank the financial support of FCT that was allowed me to dedicate the initial 4 years pursuing this research (PhD grant SFRH/BD/19753/2004). I would also like to thank CRACS/INESC-Porto L.A. for support on the fifth year, allowing me to further improve my work and finish my PhD.
    [Show full text]
  • Annotated Hypergraphs: Models and Applications
    Annotated hypergraphs: models and applications The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Chodrow, Philip, and Andrew Mellor. “Annotated Hypergraphs: Models and Applications.” Applied Network Science 5, 1 (December 2020): 9. © 2020 The Authors As Published http://dx.doi.org/10.1007/s41109-020-0252-y Publisher Springer Science and Business Media LLC Version Final published version Citable link https://hdl.handle.net/1721.1/124666 Terms of Use Creative Commons Attribution 4.0 International license Detailed Terms https://creativecommons.org/licenses/by/4.0/ Chodrow and Mellor Applied Network Science (2020) 5:9 Applied Network Science https://doi.org/10.1007/s41109-020-0252-y RESEARCH Open Access Annotated hypergraphs: models and applications Philip Chodrow1*† and Andrew Mellor2† *Correspondence: [email protected] Abstract †Philip Chodrow and Andrew Mellor Hypergraphs offer a natural modeling language for studying polyadic interactions contributed equally to this work. 1Operations Research Center, between sets of entities. Many polyadic interactions are asymmetric, with nodes Massachusetts Institute of playing distinctive roles. In an academic collaboration network, for example, the order Technology, 77 Massachusetts of authors on a paper often reflects the nature of their contributions to the completed Avenue, 02139 Cambridge, MA, USA Full list of author information is work. To model these networks, we introduce annotated hypergraphs as natural available at the end of the article polyadic generalizations of directed graphs. Annotated hypergraphs form a highly general framework for incorporating metadata into polyadic graph models. To facilitate data analysis with annotated hypergraphs, we construct a role-aware configuration null model for these structures and prove an efficient Markov Chain Monte Carlo scheme for sampling from it.
    [Show full text]
  • Snapshot: Network Motifs Oren Shoval and Uri Alon Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
    SnapShot: Network Motifs Oren Shoval and Uri Alon Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel 326 Cell 143, October 15, 2010 ©2010 Elsevier Inc. DOI 10.1016/j.cell.2010.09.050 See online version for legend and references. SnapShot: Network Motifs Oren Shoval and Uri Alon Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel Transcription regulation and signaling networks are composed of recurring patterns called network motifs. Network motifs are much more prevalent in biological networks than would be expected by comparison to random networks and comprise almost the entire network structure. The same small set of network motifs has been found in diverse organ- isms ranging from bacteria to plants to humans. Experiments show that each network motif can carry out specific dynamic functions in the computation done by the cell. Here we review the main classes of network motifs and their biological functions. Autoregulation Negative autoregulation (NAR) occurs when a transcription factor represses the transcription of its own gene. (We use transcription to help make examples concrete; all circuits described here could operate also in other regulatory modes, e.g., a protein inhibiting its own activity by autophosphorylation.) This occurs in about half of the repressors in E. coli and can speed up the response time of gene circuits and reduce cell–cell variation in protein levels that are due to fluctuations in production rate. Positive autoregulation occurs when a transcription factor enhances its own rate of production. Response times are slowed and variation is usually enhanced. This motif, given sufficient cooperativity, can lead to bimodal (all-or-none) distributions, where the concentration of X is low in some cells but high in others.
    [Show full text]
  • Annotated Hypergraphs: Models and Applications
    Chodrow and Mellor Applied Network Science (2020) 5:9 Applied Network Science https://doi.org/10.1007/s41109-020-0252-y RESEARCH Open Access Annotated hypergraphs: models and applications Philip Chodrow1*† and Andrew Mellor2† *Correspondence: [email protected] Abstract †Philip Chodrow and Andrew Mellor Hypergraphs offer a natural modeling language for studying polyadic interactions contributed equally to this work. 1Operations Research Center, between sets of entities. Many polyadic interactions are asymmetric, with nodes Massachusetts Institute of playing distinctive roles. In an academic collaboration network, for example, the order Technology, 77 Massachusetts of authors on a paper often reflects the nature of their contributions to the completed Avenue, 02139 Cambridge, MA, USA Full list of author information is work. To model these networks, we introduce annotated hypergraphs as natural available at the end of the article polyadic generalizations of directed graphs. Annotated hypergraphs form a highly general framework for incorporating metadata into polyadic graph models. To facilitate data analysis with annotated hypergraphs, we construct a role-aware configuration null model for these structures and prove an efficient Markov Chain Monte Carlo scheme for sampling from it. We proceed to formulate several metrics and algorithms for the analysis of annotated hypergraphs. Several of these, such as assortativity and modularity, naturally generalize dyadic counterparts. Other metrics, such as local role densities, are unique to the setting of annotated hypergraphs. We illustrate our techniques on six digital social networks, and present a detailed case-study of the Enron email data set. Keywords: Hypergraphs, Null models, Network science, Statistical inference, Community detection Introduction Many data sets of contemporary interest log interactions between sets of entities of vary- ing size.
    [Show full text]