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![Chinese Whispers [Biemann]
• a
Randomized graph-clustering algorithm, which is time-linear in the
number of edges.
It can be viewed as a simulation of an agent-based social network.
DIME 2014, FEUP, 5-12-2014 20
Gephi plugin](https://image.slidesharecdn.com/networkanalysis-141202131214-conversion-gate02/85/Network-analysis-20-320.jpg)
Uploaded byFrancisco Restivo
Network analysis
The document discusses network analysis and provides an overview of the topic. It covers that networks are found everywhere, from social and biological systems to financial networks. It also discusses complex networks, influential nodes, communities, and software tools for network analysis. A variety of network analysis methods and algorithms are presented, including approaches for identifying communities, central nodes, and other network properties. Examples of real-world networks and potential projects for network analysis are also mentioned.
Network analysis
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- 2. Agenda • Networks areeverywhere • Social, biological, financial, etc • Complex networks • Influential nodes & communities • Who’s who • Software tools • Some project ideas 2DIME 2014, FEUP, 5-12-2014
- 3. Networks that changedthe world DIME 2014, FEUP, 5-12-2014 3
- 4. More… DIME 2014, FEUP,5-12-2014 4
- 5. Data, information andknowledge DIME 2014, FEUP, 5-12-2014 5 http://www.knowledge-management-tools.net/knowledge-information-data.html
- 6. DIME 2014, FEUP,5-12-2014 6 Retweet Network – #DE2012 raminetinati.wordpress.com
- 7. DIME 2014, FEUP,5-12-2014 7 ProteinNetwork http://intbio.ncl.ac.uk/?people=dr-katherine-james
- 8. DIME 2014, FEUP,5-12-2014 8 Networkviewofcross-borderbankingin2007 www.fna.fi
- 9. Networks • Vertexes (nodes,actors) – properties (attributes) • Edges (relations) – directed/undirected – properties (attributes) • Density • Excentricity • Diâmeter DIME 2014, FEUP, 5-12-2014 9 9/21=0.43 3 2 3 2 3 3 2 3
- 10. Adjacency matrix DIME 2014,FEUP, 5-12-2014 10 01001 10101 01011 00101 11110 A 00000 10000 01000 00100 11110 A 21 V={Po, Lx, Ma, Pa, Be}
- 11. Influential nodes • Degree(in, out) • Clustering coefficient • Centrality (degree, closeness, betweenness) • PageRank • etc 11DIME 2014, FEUP, 5-12-2014 http://drunksandlampposts.files.wordpress.com/2012/06/philprettyv4.png?
- 12. Understanding node metrics LabelDegree Betweenness Centrality Closeness Centrality PageRank Clustering Coefficient Por 3 2.000 0.100 1.129 0.333 Lis 2 0.000 0.083 0.791 1.000 Mad 3 3.500 0.111 1.132 0.333 Par 4 7.000 0.125 1.537 0.167 Ber 2 0.000 0.091 0.798 1.000 Rom 1 0.000 0.077 0.477 0.000 Lon 3 2.500 0.111 1.136 0.333 DIME 2014, FEUP, 5-12-2014 12 1 – Lis – Par 1 – Lis – Rom 0.5 – Lis – Ber 0.5 – Por – Par 0.5 – Por – Rom
- 13. Understanding networks • Formation •Modularity • Communities • Network dynamics • etc DIME 2014, FEUP, 5-12-2014 13 http://www.freerepublic.com/focus/news/1327834/posts
- 14. Degree distribution DIME 2014,FEUP, 5-12-2014 14 0.00 0.05 0.10 0.15 0.20 0 1 2 3 4 5 6 7 8 9 10
- 15. Community Detection • Communitiesand clusters are different • Network data is related to graph properties • Real world means big data DIME 2014, FEUP, 5-12-2014 15
- 16. Modularity • Compares numberof edges with number of edges of a random network • Maximize Q is NP-hard DIME 2014, FEUP, 5-12-2014 16 j g, i g ij ij P ij A m2 1 Q m2 j k i k ij P
- 17. Clauset-Newman-Moore A hierarchical agglomerationalgorithm for detecting community structure which is faster than many competing algorithms. Its running time on a network with n vertices and m edges is O(md log n) where d is the depth of the dendrogram describing the community structure. DIME 2014, FEUP, 5-12-2014 17 NodeXL
- 18. Wakita-Tsurumi CNM algorithm doesnot scale well and its use is practically limited to networks whose sizes are up to 500,000 nodes. A simple heuristics that attempts to merge community structures in a balanced manner can dramatically improve community structure analysis. DIME 2014, FEUP, 5-12-2014 18 NodeXL
- 19. Girvan-Newman A property thatis found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. DIME 2014, FEUP, 5-12-2014 19 NodeXL
- 20. Chinese Whispers [Biemann] •a Randomized graph-clustering algorithm, which is time-linear in the number of edges. It can be viewed as a simulation of an agent-based social network. DIME 2014, FEUP, 5-12-2014 20 Gephi plugin
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- 26. Figure 5. Mapof science derived from clickstream data. Bollen J, Van de Sompel H, Hagberg A, Bettencourt L, et al. (2009) Clickstream Data Yields High-Resolution Maps of Science. PLoS ONE 4(3): e4803. doi:10.1371/journal.pone.0004803 http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004803
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- 29. DIME 2014, FEUP,5-12-2014 29 How to generate journal insights using visualization techniques
- 30. Software Tools • NodeXL •Gephi • D3.js (JavaScript) • NetworkX (Python) • Netlogo • etc DIME 2014, FEUP, 5-12-2014 30 http://gephi.github.io/features/
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- 38. Datasets • netvizz • Ikeep my collection here https://sites.google.com/site/frestivo/networked-life/databases • There is another in Quora Where can I find large datasets open to the public? DIME 2014, FEUP, 5-12-2014 38
- 39. netvizz DIME 2014, FEUP,5-12-2014 39
- 40. DIME 2014, FEUP,5-12-2014 40 netvizz > gephi > NodeXL 0 50 100 150 200 250 Frequency Degree Minimum Degree 0 Maximum Degree 237 Average Degree 20.012 Median Degree 14.000
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- 43. DIME 2014, FEUP,5-12-2014 43 Digital footprint…
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- 47. Project approach • Bigdata set • Think if communities make sense • Compare different approaches • Explain your findings DIME 2014, FEUP, 5-12-2014 47
- 48. Thank you! DIME 2014,FEUP, 5-12-2014 48