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

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