Regular Equivalence for Social Networks
Abstract
:1. Introduction
2. Background and Context
3. Materials and Methods
3.1. Graphs and Equivalence Relations
3.2. Regular Equivalence Algorithm
3.2.1. Description of the Algorithm
Algorithm 1 Regular equivalence. |
Input: Graph G Output: Minimal regular coloring function c 1: for do end for 2: 3: repeat 4: for do end for 5: for do 6: Calculate 7: 8: end for 9: if then 10: break repeat-loop 11: end if 12: 13: 14: for do 15: for do end for 16: 17: end for 18: end repeat 19: return c |
3.2.2. Proof of Correctness
3.3. Computational Complexity
4. Results
4.1. Runtimes for Calculating Regular Equivalences
4.2. Experiments
4.2.1. Random Barabási–Albert Graphs
Algorithm 2 Relation between & to have many colors |
Input: Output: 1: 2: 3: while 4: 5: for 1 to 100 do 6: 7: 8: if then 9: 10: end if 11: end for 12: if then 13: 14: else 15: 16: end if 17: 18: end while 19: return |
4.2.2. Real-Life Social Networks
- CA-AstroPh: This graph represents a collaboration network on astrophysics obtained from arXiv. It covers scientific collaborations between authors that submitted papers within the Astrophysics category. If two authors u and v co-authored a paper, the graph will contain an (undirected) edge . It will thus contain a small complete subgraph for every paper in the database, as all authors on a single paper always induce a complete graph for these authors.
- CA-CondMat: This dataset corresponds to the collaboration network obtained from arXiv within the Condense Matter Physics category. It was derived in a similar way as the CA-AstroPh network. It has about more vertices than CA-AstroPh but only about half the number of edges.
- Email-Enron: This is an email-communication network, covering email communication within the company Enron, and consisting of about half a million emails. It was disclosed to the public by the Federal Energy Regulatory Commission during the investigation into the Enron scandal in 2001. Vertices represent email-addresses, and an undirected edge is added to the graph if either u or v sent at least one email to v or u, respectively. Thus, the original direction of communication is not available in the dataset.
- Slashdot0811: This dataset represents a social network derived from the website Slashdot, which is a news website focusing on technology-related subjects. Users can post messages and tag each other as friend or foe. They are represented by vertices and the tags are represented as undirected edges. No distinction is made between friend-tags and foe-tags, so an edge can have four different meanings. This explains the reduction in compared to the original dataset.
- DBLP: The DBLP-dataset is derived from the DBLP-database maintained by the University of Trier, which contains a computer science bibliography with a comprehensive list of academic research papers in different subjects all related to computer science. The dataset represents a collaboration network where two authors (vertices) are connected through an (undirected) edge if and only if they co-authored at least one paper.
- LiveJournal: This dataset was derived from LiveJournal, a website community allowing free blogs. People can befriend each other and form groups together. We consider being friends/foes a symmetric relation, and represent users as vertices and their friendships as undirected edges.
5. Discussion
Future Work
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
Seq(u,v) | vertices u and v are structurally equivalent |
Aut(A) | permutation A is an automorphism |
Aeq(u,v) | vertices u and v are automorphically equivalent |
Reg(c) | coloring function c is regular |
Minreg(c) | coloring function c is regular and minimal |
Req(u,v) | vertices u and v are regularly equivalent |
Appendix A. Barabási–Albert Graphs
- Add a new vertex to .
- Add new edges, each one between and some vertex , which is randomly chosen with a probability proportional to its degree.
- Add new edges, each one randomly chosen between vertices and , with a probability proportional to the product of their degrees.
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Network | |V| | |E| | |E|/|V| |
---|---|---|---|
CA-AstroPh | 18,772 | 198,050 | 10.6 |
CA-CondMat | 23,133 | 93,439 | 4.0 |
Email-Enron | 36,692 | 183,831 | 5.0 |
Slashdot0811 | 77,360 | 469,180 | 6.1 |
DBLP | 317,080 | 1,049,866 | 3.3 |
LiveJournal | 3,997,962 | 34,681,189 | 8.7 |
Network | |C| | |C|/|V| | Runtime (s) |
---|---|---|---|
CA-AstroPh | 14,734 | 0.9 | |
CA-CondMat | 17,131 | 0.5 | |
Email-Enron | 20,417 | 0.4 | |
Slashdot0811 | 61,457 | 1.4 | |
DBLP | 233,466 | 14.5 | |
LiveJournal | 3,703,526 | 1684 |
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Audenaert, P.; Colle, D.; Pickavet, M. Regular Equivalence for Social Networks. Appl. Sci. 2019, 9, 117. https://doi.org/10.3390/app9010117
Audenaert P, Colle D, Pickavet M. Regular Equivalence for Social Networks. Applied Sciences. 2019; 9(1):117. https://doi.org/10.3390/app9010117
Chicago/Turabian StyleAudenaert, Pieter, Didier Colle, and Mario Pickavet. 2019. "Regular Equivalence for Social Networks" Applied Sciences 9, no. 1: 117. https://doi.org/10.3390/app9010117
APA StyleAudenaert, P., Colle, D., & Pickavet, M. (2019). Regular Equivalence for Social Networks. Applied Sciences, 9(1), 117. https://doi.org/10.3390/app9010117