1 dimension matching in facebook and linkedin networks anthony bonato ryerson university toronto,...
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Dimension matching in Facebook and LinkedIn networks
Anthony BonatoRyerson University
Toronto, Canada
ICMCE 2015
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I am very happy to be back in India.
நான் இந்தியா இருக்கும் மிகவும் சந்தேதாஷமாக இருக்கிதே�ன்.
Dimension matching in OSNs 2
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Friendship networks• network of on- and off-line friends form a large
web of interconnected links
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6 degrees of separation
• (Stanley Milgram, 67): famous chain letter experiment
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6 Degrees in Facebook?• 1.55 billion users• (Backstrom et al., 2012)
– 4 degrees of separation in Facebook
– when considering another person in the world, a friend of your friend knows a friend of their friend, on average
• similar results for Twitter and other OSNs
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Complex networks in the era of Big Data
• web graph, social networks, biological networks, internet networks, …
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What is a complex network?• no precise definition• however, there is general consensus on the
following observed properties1. large scale2. evolving over time3. power law degree distributions4. small world properties
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Examples of complex networks• technological/informational: web graph, router
graph, AS graph, call graph, e-mail graph
• social: on-line social networks (Facebook, Twitter, LinkedIn,…), collaboration graphs, co-actor graph
• biological networks: protein interaction networks, gene regulatory networks, food networks
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Properties of complex networks
1. Large scale: relative to order and size
• web graph: order > trillion– some sense infinite: number of strings entered into
Google• Facebook: > 1 billion nodes; Twitter: > 307 million
nodes– much denser (ie higher average degree) than the
web graph• protein interaction networks: order in thousands
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Properties of complex networks
2. Evolving: networks change over time
• web graph: billions of nodes and links appear and disappear each day
• Facebook: grew to 1 billion users – denser than the web graph
• protein interaction networks:order in the thousands
– evolves much more slowly
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Properties of Complex Networks3. Power law degree distribution
• for a graph G of order n and i a positive integer, let Ni,n denote the number of nodes of degree i in G
• we say that G follows a power law degree distribution if for some range of i and some b > 2,
• b is called the exponent of the power law
niN bni
,
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Power laws in OSNs
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Dimension matching in OSNs 14
Graph parameters
• average distance:
• clustering coefficient:
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Properties of Complex Networks4. Small world property
• introduced by Watts & Strogatz in 1998:– low distances
• diam(G) = O(log n)• L(G) = O(loglog n)
– higher clustering coefficient than random graph with same expected degree
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Sample data: Flickr, YouTube, LiveJournal, Orkut
• (Mislove et al,07): short average distances and high clustering coefficients
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Other properties of complex networks• many complex networks (including on-line
social networks) obey two additional laws:• Densification power law (Leskovec,
Kleinberg, Faloutsos,05): – networks are becoming more dense over
time; i.e. average degree is increasing|(E(Gt)| ≈ |V(Gt)|a
where 1 < a ≤ 2: densification exponent
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• Decreasing distances (Leskovec, Kleinberg, Faloutsos,05):
– distances (diameter and/or average distances) decrease with time
(Kumar et al,06):
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Other properties• Connected component structure: emergence of
components; giant components
• Spectral properties: adjacency matrix and Laplacian matrices, spectral gap, eigenvalue distribution
• Small community phenomenon: most nodes belong to small communities (ie subgraphs with more internal than external links)
…
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Blau space
• OSNs live in social space or Blau space: – each user identified with a point in a
multi-dimensional space– coordinates correspond to socio-
demographic variables/attributes• homophily principle: the flow of
information between users is a declining function of distance in Blau space
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Underlying geometry
Feature space thesis: every complex network is naturally associated with an underlying feature space.
For eg:– web graph: topic space– OSNs: Blau space– PPIs: biochemical space
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Dimensionality
• Question: What is the dimension of the Blau space of OSNs?
• what is a credible mathematical formula for the dimension of an OSN?
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Six dimensions of separation 23
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Why model complex networks?
• uncover and explain the generative mechanisms underlying complex networks
• predict the future• nice mathematical challenges• models can uncover the hidden reality of
networks
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Networks - Bonato 25
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“All models are wrong, but some are more useful.” – G.P.E. Box
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Random geometric graphs• n nodes are randomly
placed in the unit square
• each node has a constant sphere of influence, radius r
• nodes are joined if their Euclidean distance is at most r
• G(n,r), r = r(n)
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Some properties of G(n,r)Theorem (Penrose,97) Let μ = nexp(-πr2n).1. If μ = o(1), then asymptotically almost surely (a.a.s.) G
is connected.2. If μ = Θ(1), then a.a.s. G has a component of order
Θ(n).3. If μ →∞, then a.a.s. G is disconnected.
• many other properties studied of G(n,r): chromatic number, clique number, Hamiltonicity, random walks, …
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Spatially Preferred Attachment (SPA) model(Aiello, Bonato, Cooper, Janssen, Prałat,08),
(Cooper, Frieze, Prałat,12)
• volume of sphere of influence proportional to in-degree
• nodes are added and spheres of influence shrink over time
• a.a.s. leads to power laws graphs, low directed diameter, and small separators
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Ranking models(Fortunato, Flammini, Menczer,06),
(Łuczak, Prałat, 06), (Janssen, Prałat,09) • parameter: α in (0,1)• each node is ranked 1,2, …, n by some function r
– 1 is best, n is worst
• at each time-step, one new node is born, one randomly node chosen dies (and ranking is updated)
• link probability r-α
• many ranking schemes a.a.s. lead to power law graphs: random initial ranking, degree, age, etc.
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Geometric model for OSNs• we consider a geometric
model of OSNs, where– nodes are in m-
dimensional Euclidean space
– volume of spheres of influence variable: a function of ranking of nodes
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Geometric Protean (GEO-P) Model(Bonato, Janssen, Prałat, 12)
• parameters: α, β in (0,1), α+β < 1; positive integer m• nodes live in an m-dimensional hypercube• each node is ranked 1,2, …, n by some function r
– 1 is best, n is worst – we use random initial ranking
• at each time-step, one new node v is born, one randomly node chosen dies (and ranking is updated)
• each existing node u has a region of influence with volume
• add edge uv if v is in the region of influence of u nr
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Notes on GEO-P model
• models uses both geometry and ranking• number of nodes is static: fixed at n
– order of OSNs at most number of people (roughly…)
• top ranked nodes have larger regions of influence
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Simulation with 5000 nodes
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Simulation with 5000 nodes
random geometric GEO-P
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Properties of the GEO-P model (Bonato, Janssen, Prałat, 2012)
• a.a.s. the GEO-P model generates graphs with the following properties:– power law degree distribution with exponent
b = 1+1/α– average degree d = (1+o(1))n(1-α-β)/21-α
• densification– diameter D = nΘ(1/m)
• small world: constant order if m = Clog n– bad spectral expansion and high clustering coefficient
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Dimension of OSNs
• given the order of the network n and diameter D, we can calculate m
• gives formula for dimension of OSN:
Dnm
loglog
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Logarithmic Dimension Hypothesis
In an OSN of order n and diameter D, the dimension of its Blau space is
• posed independently by (Leskovec,Kim,11), (Frieze, Tsourakakis,11)
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Dn
loglog
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Uncovering the hidden reality• reverse engineering approach
– given network data (n, D), dimension of an OSN gives smallest number of attributes needed to identify users
• that is, given the graph structure, we can (theoretically) recover the social space
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6 Dimensions of Separation
OSN Dimension
Facebook 7
YouTube 6
Twitter 4
Flickr 4
Cyworld 7
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MITACS team, UBC 2012
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L to R: Amanda Tian, David Gleich, Myughwan Kim, Me, Stephen Young, Dieter Mitsche
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MGEO-P(Bonato, Gleich, Mitsche, Prałat, Tian, Young,14)
• time-steps in GEO-P form a computational bottleneck
• consider a GEO-P model where we forget the history of ranks– memoryless GEO-P (MGEO-P)
• place n points u.a.r. in the hypercube • assign ranks from via a random permutation σ• for each pair i > j, ij is an edge if j is in the ball of
volume σ(i)–αn-β
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Contrasting the models• by considering the evolution of ranks in GEO-P, the
probability that an edge is present in GEO-P and not in MGEO-P is:
• intuitively, the models generate similar graphs
• many a.a.s properties hold in MGEO-P with similar parameters
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)1()log( 241
22
onnO
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Properties of the MGEO-P model (BGMPTY,14)
• a.a.s. the MGEO-P model generates graphs with the following properties:– power law degree distribution with exponent
b = 1+1/α– average degree d = (1+o(1))n(1-α-β)/21-α
• densification– diameter D = nΘ(1/m)
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Proof sketch: diameter• eminent node:
– highly ranked: ranking greater than some fixed R
• partition hypercube into small hypercubes• choose size of hypercubes and R so that
– each hypercube contains at least log2n eminent nodes
– sphere of influence of each eminent node covers each hypercube and all neighbouring hypercubes
• choose eminent node in each hypercube: backbone
• show all nodes in hypercube distance at most 2 from backbone
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Back to question…
• How would we measure the dimensionality of Blau space?
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Aside: machine learning• machine learning is a branch of AI
where computers make decisions and answer questions based on data sets
• examples: – spam filters– Netflix recommender systems
• especially useful when the data or number of decisions are too large for humans to process
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Facebook100
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Validating the LDH• we tested the dimensionality of large-scale
samples from real OSN data– Facebook100 and LinkedIn (sampled over
time)• IDEA: use machine learning (SVM) to predict
dimensions– features: small subgraph counts (3- and 4-
vertex subgraphs)– compared sampled data vs simulations of
MGEO-P with dimensions 1 through 1250Dimension matching in OSNs
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Graphlets
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Experimental design
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Sample: Michigan
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Stanford3: n: 11621 edges: 568330 avgdeg: 97.81086 plexp: 3.730000 GeoP parameters alphabeta: 0.510389 alpha: 0.366300 beta: 0.144089
python geop_dim_experiment.py --logcount -s 50 -t 0 --mmax 12 --prob 0.001 Stanford3 11621 568330 0.366300 0.144089 M-GeoP dimensions: LADTree: 2 J48: 3 Logistic: 5 SVM: 5
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FB and LinkedIn - SVM
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FB and LinkedIn - Eigenvalues
56Dimension matching in OSNs
![Page 57: 1 Dimension matching in Facebook and LinkedIn networks Anthony Bonato Ryerson University Toronto, Canada ICMCE 2015](https://reader036.vdocuments.net/reader036/viewer/2022062504/5a4d1b6e7f8b9ab0599b46fe/html5/thumbnails/57.jpg)
Figure 6. For three of the Facebook networks, we show the eigenvalue histogram in red, the eigenvalue histogram from the best fit MGEO-P network in blue, and the eigenvalue histograms
for samples from the other dimensions in grey.
Bonato A, Gleich DF, Kim M, Mitsche D, et al. (2014) Dimensionality of Social Networks Using Motifs and Eigenvalues. PLoS ONE 9(9): e106052. doi:10.1371/journal.pone.0106052http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106052
![Page 58: 1 Dimension matching in Facebook and LinkedIn networks Anthony Bonato Ryerson University Toronto, Canada ICMCE 2015](https://reader036.vdocuments.net/reader036/viewer/2022062504/5a4d1b6e7f8b9ab0599b46fe/html5/thumbnails/58.jpg)
Future directions
• Other data sets
• Fractal dimension
• What are the attributes?
• What implications does LDH have for OSNs or social networks in general?
58Dimension matching in OSNs
![Page 59: 1 Dimension matching in Facebook and LinkedIn networks Anthony Bonato Ryerson University Toronto, Canada ICMCE 2015](https://reader036.vdocuments.net/reader036/viewer/2022062504/5a4d1b6e7f8b9ab0599b46fe/html5/thumbnails/59.jpg)
Thank you!
நன்றி!
Dimension matching in OSNs 59