English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Information spreads across social and technological networks, but often thenetwork structures are hidden from us and we only observe the traces left bythe diffusion processes, called cascades. Can we recover the hidden networkstructures from these observed cascades? What kind of cascades and how manycascades do we need? Are there some network structures which are more difficultthan others to recover? Can we design efficient inference algorithms withprovable guarantees?  Despite the increasing availability of cascade data and methods for inferringnetworks from these data, a thorough theoretical understanding of the abovequestions remains largely unexplored in the literature. In this paper, weinvestigate the network structure inference problem for a general family ofcontinuous-time diffusion models using an $l_1$-regularized likelihoodmaximization framework. We show that, as long as the cascade sampling processsatisfies a natural incoherence condition, our framework can recover thecorrect network structure with high probability if we observe $O(d^3 \log N)$cascades, where $d$ is the maximum number of parents of a node and $N$ is thetotal number of nodes. Moreover, we develop a simple and efficientsoft-thresholding inference algorithm, which we use to illustrate theconsequences of our theoretical results, and show that our frameworkoutperforms other alternatives in practice.

Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm