Social Network Generation

Issues on Social Network Generation for Evaluating Visualizations

Watts and Strogatz first described in (Watts, D. J. and S. H. Strogatz (1998). "Collective dynamics of 'small-world' networks." Nature 393: 440 - 442) the concept of small-world networks. They formalized these networks as graphs with three properties: power-law degree distribution, high clustering coefficient and small average shortest path. In the same paper they propose a basic model fitting these properties consisting in a grid (fixed local neighborhood) with additional links simulating some unexpected relations support to the six degrees of separation discovered by Milgram (Milgram, S. (1967). "The small world problem." Psychology Today: 60-67). Barabási and Albert proposed an incremental model to improve it (Barabási, A.-L. and R. Albert (1999). "Emergence of Scaling in Random Networks." Science 286(5439): 509 - 512. ). Since Watts and Strogatz’ model, several have been proposed each generating networks with one or two of the described properties (power-law) but none combine the three of them.

Here are some results of available generators present in the JUNG package.

Issues on Social Network Generation for Evaluating Visualizations

Watts and Strogatz first described in (Watts, D. J. and S. H. Strogatz (1998). "Collective dynamics of 'small-world' networks." Nature 393: 440 - 442) the concept of small-world networks. They formalized these networks as graphs with three properties: power-law degree distribution, high clustering coefficient and small average shortest path. In the same paper they propose a basic model fitting these properties consisting in a grid (fixed local neighborhood) with additional links simulating some unexpected relations support to the six degrees of separation discovered by Milgram (Milgram, S. (1967). "The small world problem." Psychology Today: 60-67). Barabási and Albert proposed an incremental model to improve it (Barabási, A.-L. and R. Albert (1999). "Emergence of Scaling in Random Networks." Science 286(5439): 509 - 512. ). Since Watts and Strogatz’ model, several have been proposed each generating networks with one or two of the described properties (power-law) but none combine the three of them.

Here are some results of available generators present in the JUNG package.

Small-World Generator

WattsBetaSmallWorldGenerator

Parameters: numNodes (the number of nodes in the ring lattice), beta (the probability of an edge being rewired randomly; the proportion of randomly rewired edges in a graph) and degree( the number of edges connected to each vertex; the local neighborhood size). Degree must be even.

Parameters and Resulting Graph characteristics

graphs	W1	W2	W3	W4	W5	W6	W7	W8	W9	W10	W11	W12
numNodes	47	47	47	47	47	47	47	47	47	47	47	94
beta	0.1	0.3	0.5	0.7	0.9	0.3	0.3	0.3	0.3	0.7	0.1	0.1
degree	6	6	6	6	6	2	4	8	10	4	8	8
numNodes	47	47	47	47	47	47	47	47	47	47	47	94
numEdges	282	282	282	282	282	94	188	376	470	188	376	752
components	1	1	1	1	1	2	1	1	1	1	1	1
density	0.36	0.36	0.36	0.36	0.36	0.21	0.29	0.41	0.46	0.29	0.41	0.29
clusteringCoefficient	0.51	0.25	0.15	0.09	0.12	0.23	0.25	0.32	0.38	0.07	0.53	0.52
diameter	6	4	4	4	4	-	6	4	3	5	5	6
averageShortestDistance	2.97	2.4	2.32	2.3	2.29	-	3.24	2.15	1.98	2.83	2.56	3.15
minDegree	5	4	4	3	4	1	2	5	8	2	7	6
maxDegree	8	9	9	9	9	4	6	10	13	8	10	10

GraphMl files and Pictures

W1 SmallWorld_47_0.1_6

W2 SmallWorld_47_0.3_6

File:WSmallWorldM 47 0.3 6.JPG

W3 SmallWorld_47_0.5_6

File:WSmallWorldM 47 0.5 6.JPG

W4 SmallWorld_47_0.7_6

[[image:wSmallWorldM_47_0.7_6.JPG|150px]

W5 SmallWorld_47_0.9_6

[[image:wSmallWorldM_47_0.9_6.JPG|150px]

W6 SmallWorld_47_0.3_2

[[image:wSmallWorldM_47_0.3_2.JPG|150px]

KleinbergSmallWorldGenerator

Parameters:latticeSize (the lattice size (length of row or column dimension)) and clusteringExponent (the clustering exponent parameter).

Parameters and Resulting Graph characteristics

graphs	W1	W2	W3	W4	W5	W6	W7	W8	W9	W10	W11
numNodes (sqrt)	7	7	7	7	7	7	7	10	10	10	10
clustering exponent	0.1	0.5	1	2	2.5	4	8	2	4	8	12
numNodes	49	49	49	49	49	49	49	100	100	100	100
numEdges	490	490	490	490	490	490	490	1000	1000	1000	1000
components	1	1	1	1	1	1	1	1	1	1	1
density	0.45	0.45	0.45	0.45	0.45	0.45	0.45	0.32	0.32	0.32	0.32
clusteringCoefficient	0.08	0.09	0.14	0.19	0.19	0.26	0.32	0.18	0.23	0.32	0.33
diameter	4	4	4	4	4	5	5	5	6	7	7
averageShortestDistance	2.38	2.36	2.37	2.44	2.48	2.54	2.73	3.1	3.57	3.65	3.68
minDegree	9	9	9	9	9	9	9	9	9	9	9
maxDegree	14	12	13	12	12	13	12	13	13	14	12