Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Fast approximation of centrality
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
A few good agents: multi-agent social learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
The role of clustering on the emergence of efficient social conventions
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Local computation of PageRank contributions
WAW'07 Proceedings of the 5th international conference on Algorithms and models for the web-graph
Using bloom filters to speed up HITS-like ranking algorithms
WAW'07 Proceedings of the 5th international conference on Algorithms and models for the web-graph
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Walking in facebook: a case study of unbiased sampling of OSNs
INFOCOM'10 Proceedings of the 29th conference on Information communications
Collective decision-making in multi-agent systems by implicit leadership
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 3 - Volume 3
Robust coordination in large convention spaces
AI Communications - European Workshop on Multi-Agent Systems (EUMAS) 2009
Submodularity of Influence in Social Networks: From Local to Global
SIAM Journal on Computing
Discovering Influence in Communication Networks Using Dynamic Graph Analysis
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Albatross sampling: robust and effective hybrid vertex sampling for social graphs
HotPlanet '11 Proceedings of the 3rd ACM international workshop on MobiArch
A data-based approach to social influence maximization
Proceedings of the VLDB Endowment
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
Using experience to generate new regulations
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Manipulating convention emergence using influencer agents
Autonomous Agents and Multi-Agent Systems
| Hi-index | 0.00 |
In open Multi-Agent Systems, where there is no centralised control and individuals have equal authority, ensuring cooperative and coordinated behaviour is challenging. Norms and conventions are a useful means of supporting cooperation in an emergent decentralised manner, however it takes time for effective norms and conventions to emerge. Identifying influential individuals enables the targeted seeding of desirable norms and conventions, which can reduce the establishment time and increase efficacy. Existing research is limited with respect to considering (i) how to identify influential agents, (ii) the extent to which network location imbues influence on an agent, and (iii) the extent to which different network structures affect influence. In this paper, we propose a general methodology for learning the network value of a node in terms of influence, and evaluate it using sampled real-world networks with a model of convention emergence that has realistic assumptions about the size of the convention space. We show that (i) the models resulting from our methodology are effective in predicting influential network locations, (ii) there are very few locations that can be classified as influential in typical networks, (iii) that four single metrics are robustly indicative of influence across a range of network structures, and (iv) our methodology learns which single metric or combined measure is the best predictor of influence in a given network.