2014.7.12 'Multilayer Networks'* 輪読会資料
* M. Kivela, A. Arenas, M. Barthelemy, J. P. Gleeson,
Y. Moreno, and M. A. Porter. Multilayer Networks.
arXiv:1309.7233.
AI Wolf Contest -Development of Game AI using Collective Intelligence-Fujio Toriumi
The document describes an AI Wolf Contest that aims to develop game AI agents that can play the party game "Are You a Werewolf?". The game involves incomplete information, communication, and deception. To solve the challenges, the contest employs a collective intelligence approach using competitions. An AI Wolf platform provides a common environment for agents to connect and play games. The first Werewolf Intelligence Competition was held in 2015 with preliminary and final stages involving over a million games to evaluate the agents. Analysis found the top agents had higher success rates than lower ranked agents.
2014.7.12 'Multilayer Networks'* 輪読会資料
* M. Kivela, A. Arenas, M. Barthelemy, J. P. Gleeson,
Y. Moreno, and M. A. Porter. Multilayer Networks.
arXiv:1309.7233.
AI Wolf Contest -Development of Game AI using Collective Intelligence-Fujio Toriumi
The document describes an AI Wolf Contest that aims to develop game AI agents that can play the party game "Are You a Werewolf?". The game involves incomplete information, communication, and deception. To solve the challenges, the contest employs a collective intelligence approach using competitions. An AI Wolf platform provides a common environment for agents to connect and play games. The first Werewolf Intelligence Competition was held in 2015 with preliminary and final stages involving over a million games to evaluate the agents. Analysis found the top agents had higher success rates than lower ranked agents.
4. 研究の発展
• Move beyond simple graphs
investigate more realistic frameworks
• e.g.
Edges can be directed
Edges have different strengths
Edges are active only at certain times
Edges exist only between nodes that belong to
different sets
“networks of networks”
• See Sction 2.4
4
5. categorize edges
• 一種類の関係のみのネットワークで社会システムを
表すことは現実を過度に単純化
• 複数種類の関係を用いたmultiple social network
の研究が重要
J. Scott. Social Network Analysis. SAGE Publications, 2012.
S. Wasserman and K. Faust. Social Network Analysis: Methods
and Applications. Cambridge University Press, 1994.
5
6. Multilayer network
• F. Roethlisberger and W. Dickson. Management and the
worker. Cambridge University Press, 1939.
ホーソンの実験
relations between 14 individuals via 6 different types of social
interactions
6
7. 様々なmultilayer network
• “multiplex networks”
M. Gluckman. The judicial process among the Barotse of
Northern Rhodesia. Manchester University Press, 1955.
L. M. Verbrugge. Multiplexity in adult friendships. Social Forces,
57(4):1286–1309, 1979.
• “multirelational networks”
S. Wasserman and K. Faust. Social Network Analysis: Methods
and Applications. Cambridge University Press, 1994.
• “multi-stranded” relationships
J. C. Mitchell, editor. Social Networks in Urban Situations:
Analyses of Personal Relationships in Central African Towns.
Manchester University Press, 1969.
7
8. 様々なmultilayer network
• “multilevel networks”
several types of nodes or hierarchical structures
See Section 2.8
• exponential random graph models (ERGMs)
D. Lusher, J. Koskinen, and G. Robins. Exponential Random Graph
Models for Social Networks. Cambridge University Press, 2013.
• meta-networks and meta-matrices
K. M. Carley and V. Hill. Structural change and learning within
organizations, 2001.
• methods for identifying social roles using
blockmodeling and relational algebras
8
9. In the computer-science and computational
linear-algebra communities
• studied various types of multilayer networks
tensor-decomposition methods
• D. M. Dunlavy, T. G. Kolda, and W. P. Kegelmeyer. Multilinear algebra for
analyzing data with multiple linkages. In J. Kepner and J. Gilbert, editors,
Graph Algorithms in the Language of Linear Algebra, Fundamentals of
Algorithms, pages 85–114. SIAM, Philadelphia, 2011.
• T. G. Kolda and B. W. Bader. Tensor decompositions and applications.
SIAM Rev., 51(3):455–500, 2009.
multiway data analysis
• E. Acar and B. Yener. Unsupervised multiway data analysis: A literature
survey. IEEE Trans. Knowl. Data Eng., 21(1):6–20, 2009.
See Section 4.2.4, 4.5.2
9
10. the singular value
decomposition (SVD)
• most widespread methods
C. D. Martin and M. A. Porter. The extraordinary SVD. Am. Math.
Monthly, 119:838–851, 2012.
• extremely successful in many applications
• used to extract communities or to rank nodes
D. M. Dunlavy, T. G. Kolda, and W. P. Kegelmeyer. Multilinear algebra for
analyzing data with multiple linkages. In J. Kepner and J. Gilbert, editors, Graph
Algorithms in the Language of Linear Algebra, Fundamentals of Algorithms,
pages 85–114. SIAM, Philadelphia, 2011.
T. Kolda and B. W. Bader. The TOPHITS model for higher-order web link
analysis. In Proceedings of the SIAM Data Mining Conference Workshop on Link
Analysis, Counterterrorism and Security, 2006.
T. G. Kolda, B. W. Bader, and J. P. Kenny. Higher-order web link analysis using
multilinear algebra. In Proceedings of the 5th IEEE International Conference on
Data Mining (ICDM 2005), pages 242–249, 2005
10
18. 2 Multilayer Networks
• the most general notion of a multilayer network
structure
by defining various constraints for that structure
• reduce the rank of a tensor
By constraining the space
“flattening” the tensor.
• 計算するために,matricesよりtensorsの方が便利
18
20. Interconnected systems
• cascading failuersで研究が進んでいる
increasing connectivity has the potential to increase
large-scale events.
monoplex networkとは異なる方法でrandom failuerを減
らす
20