2013 TraceComplexityofNetworkInferen

(Abrahao et al., 2013) ⇒ Bruno Abrahao, Flavio Chierichetti, Robert Kleinberg, and Alessandro Panconesi. (2013). “Trace Complexity of Network Inference.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487664

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Author Keywords

Design methodology; independent cascade model; network epidemics; network inference; sampling complexity

Abstract

The network inference problem consists of reconstructing the edge set of a network given traces representing the chronology of infection times as epidemics spread through the network. This problem is a paradigmatic representative of prediction tasks in machine learning that require deducing a latent structure from observed patterns of activity in a network, which often require an unrealistically large number of resources (e.g., amount of available data, or computational time). A fundamental question is to understand which properties we can predict with a reasonable degree of accuracy with the available resources, and which we cannot. We define the trace complexity as the number of distinct traces required to achieve high fidelity in reconstructing the topology of the unobserved network or, more generally, some of its properties. We give algorithms that are competitive with, while being simpler and more efficient than, existing network inference approaches. Moreover, we prove that our algorithms are nearly optimal, by proving an information-theoretic lower bound on the number of traces that an optimal inference algorithm requires for performing this task in the general case. Given these strong lower bounds, we turn our attention to special cases, such as trees and bounded-degree graphs, and to property recovery tasks, such as reconstructing the degree distribution without inferring the network. We show that these problems require a much smaller (and more realistic) number of traces, making them potentially solvable in practice.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 TraceComplexityofNetworkInferen	Flavio Chierichetti Alessandro Panconesi Bruno Abrahao Robert Kleinberg			Trace Complexity of Network Inference				10.1145/2487575.2487664		2013