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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Now, with the prevalence of social media, such as Facebook, Weibo, how to maximize influence of individuals, products, actions in new media is of practical significance. Generally, maximizing influence first needs to identify the most influential individuals since they can spread their influence to most of others in the social media. Many studies on influence maximization aimed to select a subset of nodes in static graphs once. Actually, real graphs are evolving. So, influential individuals are also changing. In these scenarios, people tend to select influential individuals multiple times instead of once. Namely, selections are raised sequentially, forming a sequence (query sequence). It raises several new challenges due to changing influential individuals. In this paper, we explore the problem of Influence Maximization over Streaming Graph (SGIM). Then, we design a compact solution for storing and indexing streaming graphs and influential nodes that eliminates the redundant computation. The solution includes Influence-Increment-Index along with two sketch-centralized indices called Influence-Index and Reverse-Influence-Index. Computing influence set of nodes will incur a large number of redundant computations. So, these indices are designed to keep track of the nodes’ influence in sketches. Finally, with the indexing scheme, we present the algorithm to answer SGIM queries. Extensive experiments on several real-world datasets demonstrate that our method is competitive in terms of both efficiency and effectiveness owing to the design of index.

Maximizing Influence Over Streaming Graphs with Query Sequence