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Social Network Analysis & Network Optimization

Social Network Analysis & Network Optimization. Dimitrios Katsaros , Ph.D. @ Dept . of Computer & Communication Engineering, University of Thessaly @ Dept . of Informatics, Aristotle University. Koblenz, February 18 th , 2008. Outline of the talk. A summary of my research

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Social Network Analysis & Network Optimization

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  1. Social Network Analysis &Network Optimization Dimitrios Katsaros, Ph.D. @ Dept. of Computer & Communication Engineering, University of Thessaly @ Dept. of Informatics, Aristotle University Koblenz, February 18th, 2008

  2. Outline of the talk • A summary of my research • Latest results: “Social Network Analysis forNetwork Optimization” • Web(2nd round review @ IEEE Transactions on Knowledge & Data Engineering) • PRIMITIVE: Community Identification • PROTOCOL: Content Outsourcing • GOAL: Latency Reduction • Wireless Multimedia Sensor Nets(2nd round review @ ACM Mobile Networks & Applications) • PRIMITIVE: “Important” Sensor Nodes Identification • PROTOCOL: Cooperative Caching • GOAL: Latency Reduction • Collective Intelligence: Latest step of cyberspace

  3. INTELLIGENCE Research areas: Ultimately  ??? Mobile/Pervasive Computing Web Pervasive Web Overlay Nets Caching & Air-Indexing Peer-to-Peer Networks Caching & Prefetching & Replication & Semistructured Data & Web views Webcasting Content Distribution Networks Location Tracking Ad Hoc Content-Based MIR Broadcasting & Data Dissemination Web Ranking & Search Engines Cooperative Caching & Sensor Node Clustering & Distributed Indexing & Coverage/Connectivity & Flash storage & Social Network Analysis Information Retrieval Sensors

  4. Social Network Analysis • A social network is a social structure to describe social relations (wikipedia) • The history of Social Network is older than everybody who is here • More than 100 years (Cooley 1909, Durkheim 1893) • Focusing on small groups • Information Techniques give it a new life [book: Stanley Wasserman & Katherine Faust] • Mathematical Representation • Structural & Locational Properties • Roles & Positions • Dyadic & Triadic Methods

  5. Social Network Analysis [Stanley Wasserman & Katherine Faust] • Mathematical Representation • Structural & Locational Properties • Centrality • Betweenness Centrality • Roles & Positions • Dyadic & Triadic Methods

  6. Betweenness Centrality • Let σuw= σwu denote the number of shortest paths from uV towV (by definition, σuu= 0) • Let σuw(v) denote the number ofshortest paths from u to w that some vertex vV lies on • TheBetweenness Centrality indexNI(v) of a vertex v is defined as: • Large values for the NI index of a node v indicate that this node can reach otherson relatively short paths, or that v lies on considerable fractions of shortestpaths connecting others

  7. The NI index in sample graphs In parenthesis, the NI index of the respective node; i.e., 7(156): node with ID 7 has NI equal to 156. • Nodes with large NI: • Articulation nodes (in bridges), e.g., 3, 4, 7, 16, 18 • With large fanout, e.g., 14, 8, U • Therefore: geodesic nodes

  8. Betweenness Centrality in … • [WEB] Performing graph clustering and recognizing communities in Web site graphs • [WIRELESS MULTIMEDIA SENSOR NETWORKS] Recognizing (in a distributed fashion) important sensor nodes, the mediators, that coordinate cooperative caching decisions

  9. Community Identification & Content Outsourcing for the Web

  10. The need for content outsourcing

  11. CiBCMethod • Target: is true • CiBC method: • Building cliques and clusters around representative (pole) nodes (with low CB) • Earlier methods have • Defined “hard communities”: node deg(inCom)>deg(outCom) • exploited “edge betweenness” to perform hierarchical agglomerative clustering

  12. 8 9 7 5 6 10 1 2 11 0 3 4 CiBCMethod Phase 1: NI Computation -O(nm) Phase 2: Initialization of cliques O(n)

  13. 8 9 7 5 6 10 1 2 11 0 3 4 CiBCMethod Phase 2: Initialization of cliques O(n)

  14. 8 9 7 5 6 10 1 2 11 0 3 4 CiBCMethod Phase 2: Initialization of cliques O(n)

  15. 8 9 7 5 6 10 1 2 11 0 3 4 CiBCMethod Phase 2: Initialization of cliques O(n)

  16. 8 9 7 5 6 10 1 2 11 0 3 4 CiBCMethod Phase 2: Initialization of cliques O(n)

  17. 8 9 7 5 6 10 1 2 11 0 3 4 CiBCMethod A Phase 3: Clique Merging &Creation of Communities B Complexity: O(l2) l is the number of cliques C D

  18. 8 9 7 5 6 10 1 2 11 0 3 4 CiBCMethod A Phase 3: Clique Merging &Creation of Communities B 4 3 C D

  19. 8 9 7 5 6 10 1 2 11 0 3 4 CiBCMethod A Phase 3: Clique Merging &Creation of Communities B C

  20. 8 9 7 5 6 10 1 2 11 0 3 4 CiBCMethod A Phase 3: Clique Merging &Creation of Communities B C

  21. 8 9 7 5 6 10 1 2 11 0 3 4 CiBCMethod A Phase 3: Clique Merging &Creation of Communities Phase4: Check constraints C

  22. CiBCvs. Clique Percolation Method, LRU

  23. Cooperative Caching in Wireless Multimedia Sensor Networks

  24. The NICoCa protocol • Each node is aware of its 2-hop neighborhood • Uses NI to characterize some neighbors as mediators • A node can be either a mediator or an ordinary node • Each sensor node stores • the dataID, and the actual multimedia datum • the data size, TTL interval • for each cached item, the timestamps of the K most recent accesses • each cached item is characterized either as O (i.e., own) or H (i.e., hosted)

  25. The cache discovery protocol (1/2) A sensor node issues a request for a multimedia item • Searches its local cache and if it is found (local cache hit) then the K most recent access timestamps are updated • Otherwise (local cache miss), the request is broadcasted and received by the mediators • These check the 2-hop neighbors of the requesting node whether they cache the datum (proximity hit) • If none of them responds (proximity cache miss), then the request is directed to the Data Center

  26. The cache discovery protocol (2/2) When a mediator receives a request, searches its cache • If it deduces that the request can be satisfied by a neighboring node (remote cache hit), forwards the request to the neighboring node with the largest residual energy • If the request can not be satisfied by this mediator node, then it does not forward it recursively to its own mediators, since this will be done by the routing protocol, e.g., AODV • If none of the nodes can help, then requested datum is served by the Data Center (global hit )

  27. Cache vs. hits (MB files & uniform access) in a dense WMSN (d = 7) HYBRID: appears at: L. Yin and G. Cao, “Supporting cooperative caching in ad hoc networks”, IEEE Transactions on Mobile Computing, 5(1):77-89, 2006

  28. Evolution of cyberspace … Collective Intelligence Net Semantic Web + Pervasive Computing WWW + Broadband + WIFI + grid computing Unicode + XML + RDF + Ontologies Internet + Multimedia + URL + HTTP + HTML Servers + Telecom Networks + PCs + TCP-IP + e-mail + FTP Computers + Micro-chips + Application Software + WYSIWYG Interfaces Transistors+Formal Logic+Digital Coding+ Program. Languages Semantic Web WWW Internet PC Computer

  29. Why Collective Intelligence? • Users/ devices generate data at an unprecedented rate • Blogs • Tags • Sensor measurements • Web pages • Rankings by search engines • They could be treated as “opinions” or “votes” • Under some conditions: group IQ > individual IQ • [So far] Opinion/Vote fusion: • PageRank (i.e., collective linking preferences) • Metasearching (ranked list merging) • Collaborative filtering (what is interesting from what other people say, what people like you say) • …..

  30. Collective Intelligence: Some challenges • Statistical analysis of social networks • Identification of influential opinions and/or producers • Discover social context to provide personalization • Opinion spam • Bias filtering

  31. Collective Intelligence: Some challenges • Finding high-quality content • Opinion mining • Dealing with controversies • Metadata from data analysis • Storage of metadata • ……………. MOST IMPORTANTLY • In Centralized and/or Distributed settings

  32. Thank you for your attention! Questions?

  33. References Our work • D. Katsaros, G. Pallis, K. Stamos, A. Sidiropoulos, A. Vakali, Y. Manolopoulos. “CDNs Content Outsourcing via Generalized Communities”. IEEE Transactions on Knowledge and Data Engineering, (under second round review), December, 2007. • N. Dimokas, D. Katsaros, and Y. Manolopoulos, “Cooperative Caching in Wireless Multimedia Sensor Networks” ACM Mobile Networks and Applications, (under second round review), February, 2008. Competing methods • [CPM community identification method] G. Palla, I.Derenyi, I.Farkas, and T.Vicsek. Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435(7043):814–818, 2005. • [Hybrid cooperative caching method] L. Yin and G. Cao. Supporting cooperative caching in ad hoc networks. IEEE Transactions on Mobile Computing, 5(1):77–89, 2006.

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