Analyzing the Facebook friendship graph S. Catanese1, P. De Meo2, E. Ferrara3, G. Fiumara1 1Dept. of Physics, Informatics Section, University of Messina 2Dept. of Computer Sciences, Vrije Universiteit Amsterdam 3Dept. of Mathematics, University of Messina 1st International Workshop on Mining the Future Internet 20 September 2010, Berlin Catanese, De Meo, Ferrara, Fiumara () Analyzing the Facebook friendship graph MIFI 2010, 20/09/2010, Berlin 1 / 42 Outline 1 Motivation Main objective The Basic Problem Classic Work 2 Our Results/Contribution Data Extraction and Cleaning Data Analysis Main Results 3 Future Issues Catanese, De Meo, Ferrara, Fiumara () Analyzing the Facebook friendship graph MIFI 2010, 20/09/2010, Berlin 2 / 42 Outline 1 Motivation Main objective The Basic Problem Classic Work 2 Our Results/Contribution Data Extraction and Cleaning Data Analysis Main Results 3 Future Issues Catanese, De Meo, Ferrara, Fiumara () Analyzing the Facebook friendship graph MIFI 2010, 20/09/2010, Berlin 3 / 42 Main objective Analyze the Facebook friendship graph using: I a wrapper (for extraction, cleaning and normalization of data) I a tool for graph visualization and analysis I developed by some of us Catanese, De Meo, Ferrara, Fiumara () Analyzing the Facebook friendship graph MIFI 2010, 20/09/2010, Berlin 4 / 42 Main objective Analyze the Facebook friendship graph using: I a wrapper (for extraction, cleaning and normalization of data) I a tool for graph visualization and analysis I developed by some of us Catanese, De Meo, Ferrara, Fiumara () Analyzing the Facebook friendship graph MIFI 2010, 20/09/2010, Berlin 4 / 42 Outline 1 Motivation Main objective The Basic Problem Classic Work 2 Our Results/Contribution Data Extraction and Cleaning Data Analysis Main Results 3 Future Issues Catanese, De Meo, Ferrara, Fiumara () Analyzing the Facebook friendship graph MIFI 2010, 20/09/2010, Berlin 5 / 42 Social Networks A Taxonomy Social Networks (SN) Described with graphs representing users and relationships among them Organizational Networks Collaboration Networks Communication Networks Friendship Networks Online Social Networks (OSNs) [1]: I Social Communities: Facebook, MySpace, etc. I Social Bookmarking: Digg, Delicious, etc. I Content Sharing: YouTube, Flickr, etc. Catanese, De Meo, Ferrara, Fiumara () Analyzing the Facebook friendship graph MIFI 2010, 20/09/2010, Berlin 6 / 42 Social Networks A Taxonomy Social Networks (SN) Described with graphs representing users and relationships among them Organizational Networks Collaboration Networks Communication Networks Friendship Networks Online Social Networks (OSNs) [1]: I Social Communities: Facebook, MySpace, etc. I Social Bookmarking: Digg, Delicious, etc. I Content Sharing: YouTube, Flickr, etc. Catanese, De Meo, Ferrara, Fiumara () Analyzing the Facebook friendship graph MIFI 2010, 20/09/2010, Berlin 6 / 42 Social Networks Examples Figure: Organizational Network Figure: Collaboration Network Figure: Friendship Network Figure: Online Social Network Catanese, De Meo, Ferrara, Fiumara () Analyzing the Facebook friendship graph MIFI 2010, 20/09/2010, Berlin 7 / 42 Mining Online Social Networks Motivation Is the distribution of friendship computable? Calculating graph properties of OSNs Exploiting new algorithms in following tasks: I Walking through a large graph (e.g. BFS, MHRW, etc.) I Data compression (matrix decomposition, quadtrees, etc.) I Efficient visualization of large graphs I Clustering data (Fruchterman-Reingold, Harel-Koren, etc.) I Optimize efficiency in metrics evaluation (e.g. All-Pairs Shortest-Paths related: BC, CC, diameter, etc.) Studying the scalability of the problem Investigating similarities between OSNs and real-life SNs Catanese, De Meo, Ferrara, Fiumara () Analyzing the Facebook friendship graph MIFI 2010, 20/09/2010, Berlin 8 / 42 Mining Online Social Networks Motivation Is the distribution of friendship computable? Calculating graph properties of OSNs Exploiting new algorithms in following tasks: I Walking through a large graph (e.g. BFS, MHRW, etc.) I Data compression (matrix decomposition, quadtrees, etc.) I Efficient visualization of large graphs I Clustering data (Fruchterman-Reingold, Harel-Koren, etc.) I Optimize efficiency in metrics evaluation (e.g. All-Pairs Shortest-Paths related: BC, CC, diameter, etc.) Studying the scalability of the problem Investigating similarities between OSNs and real-life SNs Catanese, De Meo, Ferrara, Fiumara () Analyzing the Facebook friendship graph MIFI 2010, 20/09/2010, Berlin 8 / 42 Mining Online Social Networks Motivation Is the distribution of friendship computable? Calculating graph properties of OSNs Exploiting new algorithms in following tasks: I Walking through a large graph (e.g. BFS, MHRW, etc.) I Data compression (matrix decomposition, quadtrees, etc.) I Efficient visualization of large graphs I Clustering data (Fruchterman-Reingold, Harel-Koren, etc.) I Optimize efficiency in metrics evaluation (e.g. All-Pairs Shortest-Paths related: BC, CC, diameter, etc.) Studying the scalability of the problem Investigating similarities between OSNs and real-life SNs Catanese, De Meo, Ferrara, Fiumara () Analyzing the Facebook friendship graph MIFI 2010, 20/09/2010, Berlin 8 / 42 Mining Online Social Networks Motivation Is the distribution of friendship computable? Calculating graph properties of OSNs Exploiting new algorithms in following tasks: I Walking through a large graph (e.g. BFS, MHRW, etc.) I Data compression (matrix decomposition, quadtrees, etc.) I Efficient visualization of large graphs I Clustering data (Fruchterman-Reingold, Harel-Koren, etc.) I Optimize efficiency in metrics evaluation (e.g. All-Pairs Shortest-Paths related: BC, CC, diameter, etc.) Studying the scalability of the problem Investigating similarities between OSNs and real-life SNs Catanese, De Meo, Ferrara, Fiumara () Analyzing the Facebook friendship graph MIFI 2010, 20/09/2010, Berlin 8 / 42 Mining Online Social Networks Motivation Is the distribution of friendship computable? Calculating graph properties of OSNs Exploiting new algorithms in following tasks: I Walking through a large graph (e.g. BFS, MHRW, etc.) I Data compression (matrix decomposition, quadtrees, etc.) I Efficient visualization of large graphs I Clustering data (Fruchterman-Reingold, Harel-Koren, etc.) I Optimize efficiency in metrics evaluation (e.g. All-Pairs Shortest-Paths related: BC, CC, diameter, etc.) Studying the scalability of the problem Investigating similarities between OSNs and real-life SNs Catanese, De Meo, Ferrara, Fiumara () Analyzing the Facebook friendship graph MIFI 2010, 20/09/2010, Berlin 8 / 42 Mining Online Social Networks Pros and Cons Pros: I Large-scale studies of phenomena and behaviors impossible before I Relations among users are clearly defined I Data can be automatically acquired I Huge amount of information can be mined I Several levels of granularity can be established Cons: I Large-scale mining issues I Computational and algorithmic challenges I Online friendship 6= Real-life friendship I Bias of data depends on visiting algorithm [2] Catanese, De Meo, Ferrara, Fiumara () Analyzing the Facebook friendship graph MIFI 2010, 20/09/2010, Berlin 9 / 42 Mining Online Social Networks Pros and Cons Pros: I Large-scale studies of phenomena and behaviors impossible before I Relations among users are clearly defined I Data can be automatically acquired I Huge amount of information can be mined I Several levels of granularity can be established Cons: I Large-scale mining issues I Computational and algorithmic challenges I Online friendship 6= Real-life friendship I Bias of data depends on visiting algorithm [2] Catanese, De Meo, Ferrara, Fiumara () Analyzing the Facebook friendship graph MIFI 2010, 20/09/2010, Berlin 9 / 42 Outline 1 Motivation Main objective The Basic Problem Classic Work 2 Our Results/Contribution Data Extraction and Cleaning Data Analysis Main Results 3 Future Issues Catanese, De Meo, Ferrara, Fiumara () Analyzing the Facebook friendship graph MIFI 2010, 20/09/2010, Berlin 10 / 42 Classic Work on (online or offline) SNs Milgram, Travers [3]: the Small World problem (1969-70) Zachary [4]: ’mining’ and modeling real-life SNs (1980) Kleinberg [5]: the small world problem from an algorithmic perspective (2000) Golbeck et al. [6]: social networks vs OSNs (2005) Barabasi [7], Leskovec [8], Shneiderman [9], etc.: all focusing on OSNs and their analysis (nowdays) I Online Social Network Analysis and Tools I Large-scale data mining from OSNs I Visualization of large graphs I Bias of data acquired from OSNs I Dynamics and evolution of OSNs Catanese, De Meo, Ferrara, Fiumara () Analyzing the Facebook friendship graph MIFI 2010, 20/09/2010, Berlin 11 / 42 Classic Work on (online or offline) SNs Milgram, Travers [3]: the Small World problem (1969-70) Zachary [4]: ’mining’ and modeling real-life SNs (1980) Kleinberg [5]: the small world problem from an algorithmic perspective (2000) Golbeck et al. [6]: social networks vs OSNs (2005) Barabasi [7], Leskovec [8], Shneiderman [9], etc.: all focusing on OSNs and their analysis (nowdays) I Online Social Network Analysis and Tools I Large-scale data mining from OSNs I Visualization of large graphs I Bias of data acquired from OSNs I Dynamics and evolution of OSNs Catanese, De Meo, Ferrara, Fiumara () Analyzing the Facebook friendship graph MIFI 2010, 20/09/2010, Berlin 11 / 42 Classic Work on (online or offline) SNs Milgram, Travers [3]: the Small World problem (1969-70) Zachary [4]: ’mining’ and modeling real-life