Social Graph Extraction, Integration, and Analysis

2016 ◽  
pp. 315-326
Keyword(s):  
Social Graph

Collective influence maximization for multiple competing products with an awareness-to-influence model

2021 ◽  
Vol 14 (7) ◽  
pp. 1124-1136
Author(s):  
Dimitris Tsaras ◽  
George Trimponias ◽  
Lefteris Ntaflos ◽  
Dimitris Papadias
Keyword(s):  
Diffusion Processes ◽  
Influence Maximization ◽  
Second Phase ◽  
Social Graph ◽  
Practical Applications ◽  
Influence Model ◽  
Best Response ◽  
The Social ◽  
Game Theoretic ◽  
Collective Influence

Influence maximization (IM) is a fundamental task in social network analysis. Typically, IM aims at selecting a set of seeds for the network that influences the maximum number of individuals. Motivated by practical applications, in this paper we focus on an IM variant, where the owner of multiple competing products wishes to select seeds for each product so that the collective influence across all products is maximized. To capture the competing diffusion processes, we introduce an Awareness-to-Influence (AtI) model. In the first phase, awareness about each product propagates in the social graph unhindered by other competing products. In the second phase, a user adopts the most preferred product among those encountered in the awareness phase. To compute the seed sets, we propose GCW, a game-theoretic framework that views the various products as agents, which compete for influence in the social graph and selfishly select their individual strategy. We show that AtI exhibits monotonicity and submodularity; importantly, GCW is a monotone utility game. This allows us to develop an efficient best-response algorithm, with quality guarantees on the collective utility. Our experimental results suggest that our methods are effective, efficient, and scale well to large social networks.

scholarly journals Knowledge Discovery from Social Graph Data

2016 ◽  
Vol 96 ◽  
pp. 682-691 ◽  
Author(s):  
Peter Braun ◽  
Alfredo Cuzzocrea ◽  
Carson K. Leung ◽  
Adam G.M. Pazdor ◽  
Kimberly Tran
Keyword(s):  
Knowledge Discovery ◽  
Social Graph ◽  
Graph Data

Some Other Applications in Community Graph under the Preview of Social Graph Using Graph-Mining Techniques

2017 ◽  
pp. 223-283
Author(s):  
Bapuji Rao ◽  
Sasmita Mishra ◽  
Saroja Nanda Mishra
Keyword(s):  
Data Mining ◽  
Graph Mining ◽  
Structured Data ◽  
Community Members ◽  
Compression Technique ◽  
Social Graph ◽  
Loss Of Information ◽  
Large Community ◽  
Reliable Knowledge ◽  
Day By Day

The retrieval of sub-graph from a large graph in structured data mining is one of the fundamental tasks for analyze. Visualization and analyze large community graph are challenging day by day. Since a large community graph is very difficult to visualize, so compression is essential. To study a large community graph, compression technique may be used for compression of community graph. There should not be any loss of information or knowledge while compressing the community graph. Similarly to extract desired knowledge of a particular sub-graph from a large community graph, then the large community graph needs to be partitioned into smaller sub-community graphs. The partition aims at the edges among the community members of dissimilar communities in a community graph. Sometimes it is essential to compare two community graphs for similarity which makes easier for mining the reliable knowledge from a large community graph. Once the similarity is done then the necessary mining of knowledge can be extracted from only one community graph rather than from both which leads saving of time.

Unbiased sampling in directed social graph

2010 ◽  
Author(s):  
Tianyi Wang ◽  
Yang Chen ◽  
Zengbin Zhang ◽  
Peng Sun ◽  
Beixing Deng ◽  
...  
Keyword(s):  
Social Graph
Sign in / Sign up

Export Citation Format

Share Document