Investigating Trace Equivalences in Information Networks

Abstract

Equivalences are widely used and have achieved much success in concurrent systems. Meanwhile, information networks are ubiquitous for representing many complex systems and have similar characteristics and properties to concurrent systems such that they both can be described by graphs. In order to simplify information networks, we introduce equivalence to information networks, specifically leveraging the trace equivalence to reduce the complexity of these networks. In this paper, we first define the concept of trace and trace equivalence in information networks, drawing on the similar concept of concurrent systems. We then propose a computational method for determining whether two nodes are trace equivalent in an information network. With the help of this method, we derive trace-equivalent networks from original networks. Experiments show that we are able to reduce the number of nodes in the ACM and DBLP datasets by at most 65.21% and 46.68%, respectively. Running the PathSim algorithm on the original and derived networks, the mean error is 0.0728 in ACM and 0.0446 in DBLP. Overall, the results indicate that the derived networks have fewer nodes and edges than the original networks, yet still capture the same or similar information. By using trace equivalence, we are able to simplify information networks and improve their efficiency while preserving most of their informational content.