2014
DOI: 10.1109/jstsp.2014.2328312
|View full text |Cite
|
Sign up to set email alerts
|

Multi-Layer Graph Analysis for Dynamic Social Networks

Abstract: Modern social networks frequently encompass multiple distinct types of connectivity information; for instance, explicitly acknowledged friend relationships might complement behavioral measures that link users according to their actions or interests. One way to represent these networks is as multi-layer graphs, where each layer contains a unique set of edges over the same underlying vertices (users). Edges in different layers typically have related but distinct semantics; depending on the application multiple l… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
25
0
1

Year Published

2016
2016
2023
2023

Publication Types

Select...
5
3
1

Relationship

2
7

Authors

Journals

citations
Cited by 36 publications
(26 citation statements)
references
References 26 publications
0
25
0
1
Order By: Relevance
“…13" respectively. The node pairs (1,2), (2,6), (3,4), (3,10), (4,15), (5,6), (7,5), (7,8), (7,9), (7,11), (7,20), (9,8), (9,11), (9,15), (10,13), (10,21), (11,16), (11,20), 12,18), (13,8), (13,22), (14,17), (14,22), (15,14), (15, 21), (15, 22), (16, 9), (16,14), (18, 16), (18, 17), (18, 20), (19,12), (19,16), (19,…”
Section: Example-2mentioning
confidence: 99%
See 1 more Smart Citation
“…13" respectively. The node pairs (1,2), (2,6), (3,4), (3,10), (4,15), (5,6), (7,5), (7,8), (7,9), (7,11), (7,20), (9,8), (9,11), (9,15), (10,13), (10,21), (11,16), (11,20), 12,18), (13,8), (13,22), (14,17), (14,22), (15,14), (15, 21), (15, 22), (16, 9), (16,14), (18, 16), (18, 17), (18, 20), (19,12), (19,16), (19,…”
Section: Example-2mentioning
confidence: 99%
“…Heterogeneous (information) networks were developed as a general framework to take into account multiple types of nodes and edges can be seen in [2,12,14]. A multiple layers of a social network which perform tasks such as inference, clustering, and anomaly detection proposed by [7]. Often social networks include different types of nodes i.e.…”
Section: Introductionmentioning
confidence: 99%
“…The goal of graph clustering is to group the nodes into clusters of high similarity. Applications of graph clustering, also known as community detection [1,2], include but are not limited to graph signal processing [3][4][5][6][7][8][9][10][11], multivariate data clustering [12][13][14], image segmentation [15,16], and network vulnerability assessment [17].…”
Section: Introductionmentioning
confidence: 99%
“…The multilayer network connectivity structure has been proposed and studied in [12,13,14,15,16], in particular, a two-layer network has been studied in [7] under the Laplacian dynamics. However, the information flow following a Laplacian process on a multilayer network in its most general form has yet to be studied.…”
Section: Introductionmentioning
confidence: 99%