Estimating the Node-Level Behaviors in Complex Networks From Structural Datasets

Abstract

There is an emerging class of networks that evolve endogenously based on the local characteristics and behaviors of nodes. Examples of such networks include social, economic, and peer-to-peer communication networks. The node-level behaviors determine the overall structure and performance of these networks. This is in contrast to exogenously designed networks whose structures are directly determined by network designers. To influence the performance of endogenous networks, it is crucial to understand a) what kinds of local behaviors result in the observed network structures and b) how these local behaviors influence the overall performance. The focus in this paper is on the first aspect, where information about the structure of networks is available at different points in time and the goal is to estimate the behavior of nodes that resulted in the observed structures. We use three different approaches to estimate the node-level behaviors. The first approach is based on the generalized preferential attachment model of network evolution. In the second approach, statistical regression-based models are used to estimate the node-level behaviors from consecutive snapshots of the network structure. In the third approach, the nodes are modeled as rational decision-making agents who make linking decisions based on the maximization of their payoffs. Within the decision-making framework, the multinomial logit choice model is adopted to estimate the preferences of decision-making nodes. The autonomous system (AS) level Internet is used as an illustrative example to illustrate and compare the three approaches.