Yichuan Jiang

Understanding Social Networks From a Multiagent Perspective

Social networks have recently been widely explored in many fields; these networks are composed of a set of autonomous social actors and the interaction relations among them. Multiagent computing has already been widely envisioned to be a powerful paradigm for modeling autonomous multientity systems; therefore, it is promising to connect the research on social networks and multiagent systems. In general, there are three views for research on social networks: the structure-oriented view, in which only the network structure characteristics among actors are considered, the actor-oriented view, in which only the behavior characteristics of actors are considered, and the actor-structure crossing view, in which both actors and network structures are considered and their crossing effects are explored. This survey paper mainly concerns studies on social networks that have the last two views and discusses the relationship between social networks and multiagent systems. Because coordination is critical for both multiagent systems and social networks, this paper classifies studies on social networks that are based on the coordination mechanisms among the actors in the social networks. By referring to typical types of coordination situations in multiagent systems, social networks in previous studies can be classified into three classes: cooperative social networks, noncooperative social networks, and multiple social networks; for each class, this paper reviews the existing studies and discusses the challenge issues and possible future research directions. From this survey, we find that social networks can be understood well via a multiagent coordination perspective and also that many multiagent coordination techniques can be cogently applied in research on social networks. Moreover, this paper discusses the advantages and disadvantages of the multiagent coordination perspective by comparing with other perspectives on studying social networks.

Diffusion in Social Networks: A Multiagent Perspective

In recent years, significant attention has been paid to diffusion in social networks (SNs), which is, factually, the collective behavior of a set of autonomous social actors for interacting on something in SNs (such as opinions, viruses, or innovations). While this subject has been intensively reported, there have been relatively few systematic reviews concerning the typical diffusion elements and models that are relevant to this subject. Because multiagent computing has already been widely envisioned to be a powerful paradigm for modeling the collective interactions of autonomous multientity systems. In this survey, we review diffusion in SNs through a multiagent perspective. First, we review the following essential elements in diffusion: 1) diffusion actors (who will diffuse), which can be understood to be the interacting agents; 2) diffusion media (where to be diffused), which can be understood to be the interaction environments in multiagent systems (MASs); and 3) diffusion contents (what to be diffused), which can be understood to be the interaction objects in MASs. Next, based on varying situations of diffusion elements, we review the representative diffusion models (how to diffuse), which can be understood as the decision-making mechanisms and interaction protocols in MASs. For each class of diffusion elements and models, we summarize the existing studies and discuss the challenges for solving the complex diffusion problems by applying multiagent methodologies. Finally, we discuss the advantages and disadvantages of our multiagent perspective by comparing other typical perspectives (the empirical research perspective and the theoretical perspective in empirical research), and we conclude with suggestions for further research.

A multi-agent coordination model for the variation of underlying network topology

In now multi-agent systems, the underlying networks are always dynamic and the network topologies are always changed in the operation. Therefore, the coordination of agents shall be adjusted for the dynamic network topology. Aiming at the dynamics of underlying network topology, a novel adaptive multi-agents coordination model is explored in this paper. In the paper, a series of algorithms for multi-agent task and resource negotiation are provided. The provided algorithms consider the factors of network topology and agent distribution, and can implement effective task allocation and resource negotiation for current network topology. Therefore, the adaptation of agent coordination for dynamic underlying network topology can be achieved, which is also proved by the case studies and performance analyses in the paper.

Decision Making of Networked Multiagent Systems for Interaction Structures

Networked multiagent systems are very popular in large-scale application environments. In networked multiagent systems, the interaction structures can be shaped into the form of networks where each agent occupies a position that is determined by such agents relations with others. To avoid collisions between agents, the decision of each agents strategies should match its own interaction position, so that the strategies available to all agents are in line with their interaction structures. Therefore, this paper presents a novel decision-making model for networked multiagent strategies based on their interaction structures, where the set of strategies for an agent is conditionally decided by other agents within its dependence interaction substructure. With the presented model, the resulting strategies available to all agents can minimize the collisions of multiagents regarding their interaction structures, and the model can produce the same resulting strategies for the isomorphic interaction structures. Furthermore, this paper uses a multiagent citation network as a case study to demonstrate the effectiveness of the presented decision-making model.

A Survey of Task Allocation and Load Balancing in Distributed Systems

In past decades, significant attention has been devoted to the task allocation and load balancing in distributed systems. Although there have been some related surveys about this subject, each of which only made a very preliminary review on the state of art of one single type of distributed systems. To correlate the studies in varying types of distributed systems and make a comprehensive taxonomy on them, this survey mainly categorizes and reviews the representative studies on task allocation and load balancing according to the general characteristics of varying distributed systems. First, this survey summarizes the general characteristics of distributed systems. Based on these general characteristics, this survey reviews the studies on task allocation and load balancing with respect to the following aspects: 1) typical control models; 2) typical resource optimization methods; 3) typical methods for achieving reliability; 4) typical coordination mechanisms among heterogeneous nodes; and 5) typical models considering network structures. For each aspect, we summarize the existing studies and discuss the future research directions. Through the survey, the related studies in this area can be well understood based on how they can satisfy the general characteristics of distributed systems.

Locating active actors in the scientific collaboration communities based on interaction topology analyses

While implementing a large-scale research project, it is necessary to appoint some principle scientists, and let each principle scientist lead a research group. In a scientific collaboration community, different scientists perform different roles while they implement the project, and some scientists may be more active than others; these active scientists often undertake the role of leadership or key coordinator in the project. Obviously, we should assign the role of principle scientists onto those active actors in the communities. In this paper, we present the model and algorithms for locating active actors in the community based on the analyses of scientists’ interaction topology, the actors with high connection degrees in the interaction topology can be considered as active ones. Finally, we make some case studies for our model and algorithms.

An adaptive adjusting mechanism for agent distributed blackboard architecture

Abstract Distributed blackboard is one of the popular agent communication architectures. However, in current agent systems, the distributed blackboard architecture is kept fixed after its initial setting, which may influence the system performance when network topology or agent cooperation relations are changed during operation. To solve the problem, this paper presents a novel mechanism for adjusting agent communication architecture. Based on graph theory, this mechanism provides a way to adjust the distributed blackboard architecture. The adjustment made to the architecture kept its validity, and the adjusted architecture outperforms the initial one in new network topology or agents cooperation relations, which are proved by the Mobile Ambients Calculus analysis and the simulation experiments. Therefore, the adjusting mechanism presented here can achieve the adaptation of the agent communication architecture to the changes of the network topology and agent cooperation relations.

Locality-sensitive task allocation and load balancing in networked multiagent systems: Talent versus centrality

With the development of large scale multiagent systems, agents are always organized in network structures where each agent interacts only with its immediate neighbors in the network. Coordination among networked agents is a critical issue which mainly includes two aspects: task allocation and load balancing; in traditional approach, the resources of agents are crucial to their abilities to get tasks, which is called talent-based allocation. However, in networked multiagent systems, the tasks may spend so much communication costs among agents that are sensitive to the agent localities; thus this paper presents a novel idea for task allocation and load balancing in networked multiagent systems, which takes into account both the talents and centralities of agents. This paper first investigates the comparison between talent-based task allocation and centrality-based one; then, it explores the load balancing of such two approaches in task allocation. The experiment results show that the centrality-based method can reduce the communication costs for single task more effectively than the talent-based one, but the talent-based method can generally obtain better load balancing performance for parallel tasks than the centrality-based one.

Concurrent Collective Strategy Diffusion of Multiagents: The Spatial Model and Case Study

Strategy diffusion is a common phenomenon in the collective motion of multiagents, which is the large scale of strategy penetrations of certain agents on other agents; there are many kinds of diffusion forms; among them, the collective diffusion is always seen, which implies that a social strategy accepted by collective agents may have strong authority and tend to diffuse to other agents. This paper presents a novel spatial model for the collective strategy diffusion in multiagent societies. In the model, the social distance between agents can be measured in a Euclidian space; the authority of a social strategy is determined by not only the number but also the collective social positions of its overlaid agents; social strategies that have strong authorities are impressed on the other agents, and the agents will accept (partially or in full) or reject them based on their own social strategies and social positions. Moreover, the paper also considers the concurrent form in the collective diffusion and presents that an agents social strategy is influenced not only by the diffusion that bears on itself but also by other concurrent diffusion processes that bear on other agents, and an agent will incline to the average social strategy of the whole system, which can make the system more unified. Finally, the paper uses queue orientation as a case to study the presented model.

Autonomous trust construction in multi-agent systems: a graph theory methodology

Trust mechanism always has two popular architectures: centralized fashion and distributed fashion. However, those two architectures are not well suited for multi-agent system since they cannot achieve the trust management autonomy. To achieve the trust management autonomy, the paper presents an autonomous trust construction model based on graph theory methodology. The presented model adopts the graph to describe the trust information, and uses the graph combination and path searching to construct the trust relation. Every agent can implement trust management autonomously; agent system can construct the global trust concept by the combination of trust information among agents; an agent can achieve the trust relation with other agent by trust path searching or trust negotiation. The simulation experiment results prove that the autonomous trust construction based on graph theory methodology is effective.