The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Effects of interaction topology and activation regime in several mutli-agent systems
MABS 2000 Proceedings of the second international workshop on Multi-agent based simulation
Extracting reputation in multi agent systems by means of social network topology
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Reputation and social network analysis in multi-agent systems
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
IEEE Intelligent Systems
Agent-organized networks for dynamic team formation
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Trust evaluation through relationship analysis
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Real-world oriented information sharing using social networks
GROUP '05 Proceedings of the 2005 international ACM SIGGROUP conference on Supporting group work
POLYPHONET: an advanced social network extraction system from the web
Proceedings of the 15th international conference on World Wide Web
A multi-agent system that facilitates scientific publications search
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Trust-based agent community for collaborative recommendation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Memetic networks: analyzing the effects of network properties in multi-agent performance
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
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Recent studies have shown that various models can explain the emergence of complex networks, such as scale-free and small-world networks. This paper presents a different model to generate complex networks using a multi-agent approach. Each node is considered as an agent. Based on voting by all agents, edges are added repeatedly. We use four different kinds of centrality measures as a utility functions for agents. Depending on the centrality measure, the resultant networks differ considerably: typically, closeness centrality generates a scale-free network, degree centrality produces a random graph, betweenness centrality favors a regular graph, and eigenvector centrality brings a complete subgraph. The importance of the network structure among agents is widely noted in the multi-agent research literature. This paper contributes new insights into the connection between agents' local behavior and the global property of the network structure. We describe a detailed analysis on why these structures emerge, and present a discussion of the possible expansion and application of the model.