Tracking Influential Nodes in Time-Decaying Dynamic Interaction Networks

TL;DR

This paper introduces a novel time-decaying dynamic interaction network model and efficient streaming algorithms to identify influential nodes in highly dynamic networks, outperforming existing methods in speed and accuracy.

Contribution

It proposes a general TDN model for dynamic influence analysis and three algorithms with theoretical guarantees for identifying influential nodes efficiently.

Findings

Algorithms achieve constant factor approximation guarantees.

Methods find near-optimal influential nodes.

Experiments show 5-15 times speedup over baselines.

Abstract

Identifying influential nodes that can jointly trigger the maximum influence spread in networks is a fundamental problem in many applications such as viral marketing, online advertising, and disease control. Most existing studies assume that social influence is static and they fail to capture the dynamics of influence in reality. In this work, we address the dynamic influence challenge by designing efficient streaming methods that can identify influential nodes from highly dynamic node interaction streams. We first propose a general time-decaying dynamic interaction network (TDN) model to model node interaction streams with the ability to smoothly discard outdated data. Based on the TDN model, we design three algorithms, i.e., SieveADN, BasicReduction, and HistApprox. SieveADN identifies influential nodes from a special kind of TDNs with efficiency. BasicReduction uses SieveADN as a basic building block to identify influential nodes from general TDNs. HistApprox significantly improves the efficiency of BasicReduction. More importantly, we theoretically show that all three algorithms enjoy constant factor approximation guarantees. Experiments conducted on various real interaction datasets demonstrate that our approach finds near-optimal solutions with speed at least $5$ to $15$ times faster than baseline methods.