The Filter-Placement Problem and its Application to Minimizing Information Multiplicity

TL;DR

This paper introduces the Filter Placement problem to strategically position nodes in information networks as filters, reducing redundant data transmission and processing, thereby improving network efficiency.

Contribution

It formally defines the Filter Placement problem, analyzes its complexity, and provides approximation algorithms and heuristics with experimental validation.

Findings

Small number of filters significantly reduces redundancy

Algorithms perform well on synthetic and real networks

Filtering improves network efficiency and reduces overhead

Abstract

In many information networks, data items -- such as updates in social networks, news flowing through interconnected RSS feeds and blogs, measurements in sensor networks, route updates in ad-hoc networks -- propagate in an uncoordinated manner: nodes often relay information they receive to neighbors, independent of whether or not these neighbors received the same information from other sources. This uncoordinated data dissemination may result in significant, yet unnecessary communication and processing overheads, ultimately reducing the utility of information networks. To alleviate the negative impacts of this information multiplicity phenomenon, we propose that a subset of nodes (selected at key positions in the network) carry out additional information filtering functionality. Thus, nodes are responsible for the removal (or significant reduction) of the redundant data items relayed through them. We refer to such nodes as filters. We formally define the Filter Placement problem as a combinatorial optimization problem, and study its computational complexity for different types of graphs. We also present polynomial-time approximation algorithms and scalable heuristics for the problem. Our experimental results, which we obtained through extensive simulations on synthetic and real-world information flow networks, suggest that in many settings a relatively small number of filters are fairly effective in removing a large fraction of redundant information.