Social Network Mining An Introduction
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1 Social Network Mining An Introduction Jiawei Zhang Assistant Professor Florida State University Big Data
2 A Questionnaire Please raise your hands, if you (1) use Facebook (2) use Instagram (3) use Snapchat (4) use LinkedIn (5) don t use any online social networks
3 Background Knowledge Big Data
4 Online Social Networks & Web 2.0 Web 1.0 vs Web 2.0 Web 1.0: users take information Web 2.0: users create information Online Social Networks & Web 2.0 OSNs use Web 2.0 technology to share a user-focused approach for communication User actively participate in content creation and editing through open collaboration between members of communities of practice
5 Online Social Networks Online social media is the use of electronic and Internet technology for the purpose of sharing and discussing information and experiences with other human beings in more effective and efficient ways.
6 New Social Networks Emerge Every Year launch year
7 Social Media Landscape In Recent Years
8 Social Network Statistics and Functions
9 Social Networks are Very BIG (Statistical Data 2017) 14 (in millions)
10 Social Networks are Very BIG ( ) (in billions)
11 Social Media Types
12 Online Social Networks
13 Blogging
14 Microblogging
15 Wiki
16 Media Sharing
17 Opinion, Review, and Rating Websites
18 Question & Answer Sites
19 An Observation: People are using multiple online social networks simultaneously 93% 53% 83% [1] Duggan et al. Social media update, 2013.
20 Network Structure Representation Big Data
21 Concept Definition: Homogeneous Social Networks
22 Concept Definition: Heterogeneous Social Networks Representation: Homogeneous Social Networks Where V is the sets of various kinds of nodes in the network and set E denotes the various types of links in the network
23 Social Network Structures are Very Complex Who, Where, When, What Locations Social Links Temporal Activities 8 AM 12 PM 4 PM 8 PM 11 PM Contents: Tweets
24 Concept Definition: Heterogeneous Social Networks Representation: Heterogeneous Social Networks where V = S is the sets of various kinds of i V i nodes in the network and E = S j E j is the set of various types of links in the network timestamps posts words locations V = {user node set, post node set,... word node set, time node set, contain contain contain contain location node set} write write write write write contain contain contain contain... attach attach attach attach attach E = {user-user link set, user-post link set, post-word link set, post-time link set, post-location link set}
25 Social Networks Belong to Different Varieties Temporal Activities Temporal Activities anchor links User Accounts 8 AM 12 PM 4 PM 8 PM 11 PM Locations User Accounts 8 AM 12 PM 4 PM 8 PM 11 PM Locations locate locate Tips Tweets
26 Concept Definition: Multiple Aligned Heterogeneous Social Networks Representation: Multiple Aligned Heterogeneous Social Networks G = (G (1),G (2) ), (A (1,2) ) Heterogeneous Social Networks G (1) : Foursquare G (2) : Twitter Anchor Links A (1,2) : Anchor links between Foursquare and Twitter
27 Social Network Mining Problems (An Overview) Big Data
28 Social Network Mining Problems: An Overview User-Centric Content-Centric Interdisciplinary Role Analysis Social Spammer Detection Social Ties Negative Links Information Diffusion Network Alignment Network Summarization Network Embedding Misinformation Event Detection Content Quality and Popularity Sentiment Analysis Social Tags Social Summarization Social Recommendations Social Media Q&A Personality Analysis Crisis Informatics Social Media Healthcare Social Media Privacy and Security Social Media Education Computational Social Science Social Media Marketing Social Media Visualization
29 Social Spammer Detection Collective Spammer Detection in Evolving Multi-Relational Social Networks
30 Social Ties Prediction
31 Supervised Link Prediction Setting network structure? u5 u4 u3 u2 u1 existing links non-existing links information used to extract feature vectors for these links link features label (u1, u2) +1 (u3, u5) +1 (u3, u4) -1 (u4, u2) -1 link to be predicted (u5, u4) supervised learning model label/score
32 Information Diffusion
33 Social Network Alignment Temporal Activities Temporal Activities User Accounts 8 AM 12 PM 4 PM 8 PM Locations 11 PM User Accounts anchor links? 8 AM 12 PM 4 PM 8 PM Locations locate? locate Tips? Tweets 11 PM
34 Network Embedding
35 Network Embedding Network 1 x j, (1),o 7 (1),o+1 yi (1) xi, 0 (1),o (2),o+1 y j yi, 0 Network Fusion Component (1) xi, 1 7 (1),1 yi, 7 (1) (2),o+1 yj 0 (2),1 yj, 0 (2) 7 (2) zj (2),o yj, xi, 7 (2),o y j, (1) zi (1),o+1 y i y i, 0 1 (2) x j, 7 (2),1 y j, 0 (1),1 y i, (2) x j, (2) (1) x i, 7 (1) x i, 1 (1) x i, 0 Network 2 xj, 0 (2) x j, 1 (2) xj, 7
36 Social Recommendation
37 Social Event Detection 2015 Twitter Channels for Rossano Flood
38 Social Network Mining Problems: An Overview User-Centric Content-Centric Interdisciplinary Role Analysis Social Spammer Detection Social Ties Negative Links Information Diffusion Network Alignment Network Summarization Network Embedding Misinformation Event Detection Content Quality and Popularity Sentiment Analysis Social Tags Social Summarization Social Recommendations Social Media Q&A Personality Analysis Crisis Informatics Social Media Healthcare Social Media Privacy and Security Social Media Education Computational Social Science Social Media Marketing Social Media Visualization
39 An Advertisement: CIS Social Network Mining Call for Enrollment Big Data
40 CIS Call for Enrollment
41 CIS Course Page:
42 Social Network Mining: An Introduction Thank You! Jiawei Zhang Big Data Florida State University
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