Towards a hybrid algorithm for extracting maximal frequent conceptual links in social networks

Stattner, Erick; Collard, Martine

doi:10.1109/rcis.2013.6577712

Cited by 6 publications

(12 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, if a frequent link is added to the result list (the list of maximum frequent links) all frequent sub-links will be deleted from the list. In [5], H-MFCL Min algorithm was presented. In this algorithm, to accelerate the extraction of MFCLs, some of the item sets are filtered.…”

Section: Frequent Sub-graph Mining That Focuses On Extractingmentioning

confidence: 99%

“…The proposed algorithm by developing the algorithm in [5] by applying feature 3 reduces the search space to extract FCL. The pseudo code for this algorithm is given below.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

“…Similar to the algorithm H-MFCL Min, input parameters are α and β that are respectively threshold value related to set of items and supporting links. The same as H-MFCL Min algorithm in [5], in the first step (t = 1), single-item item set ( ) More exactly, this search is done so that for every item collection …”

Section: The Proposed Algorithmmentioning

confidence: 99%

“…In the field of complex data structures such as networks, the issue of exploring frequent items is discussed in form of finding a subset of nodes (sub-graphs) that occur frequently arises in a network known as graph mining. Although primitive methods in this field have been using measures deriving from graph theory [2], new approaches known as social networks mining or simply link mining try to examine features of node in addition to the network structure to extract a new set of patterns [3][4][5][6] described a new approach as conceptual link to describe social networking. Conceptual link provides the knowledge about groups of nodes connected to each other in a dense social network, and through a reduced structure, which is called as conceptual view, leads to a meaning display of social network.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Offering a New Approach for Extracting Recurring Conceptual Links from Social Networks

Tabatabaee

2018

IRATJ

View full text Add to dashboard Cite

Massive amounts of data in social networks have made researchers look for ways to display a summary of the information provided and extract knowledge from them. Basic methods in the field used the criteria coming from graph theory, but new approaches are trying to take advantage of the realm of traditional data exploration and linked data. One of these new approaches is the approach called conceptual link approach, which was introduced to describe the social networks. In this approach, using the concept of contextual links, knowledge of the social network through a conceptual view of the dramatic structure is summarized as means of social networking. Conceptual perspective provides a summary of existing knowledge on a social network.

show abstract

Section: Frequent Sub-graph Mining That Focuses On Extractingmentioning

confidence: 99%

“…The proposed algorithm by developing the algorithm in [5] by applying feature 3 reduces the search space to extract FCL. The pseudo code for this algorithm is given below.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

Section: The Proposed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Offering a New Approach for Extracting Recurring Conceptual Links from Social Networks

Tabatabaee

2018

IRATJ

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

D-MFCLMin: A New Algorithm for Extracting Frequent Conceptual Links from Social Networks

Tabatabaee¹

2017

ijacsa

View full text Add to dashboard Cite

Abstract-Massive amounts of data in social networks have made researchers look for ways to display a summary of the information provided and extract knowledge from them. One of the new approaches to describe knowledge of the social network is through a concise structure called conceptual view. In order to build this view, it is first needed to extract conceptual links from the intended network. However, extracting these links for large scale networks is very time consuming. In this paper, a new algorithm for extracting frequent conceptual link from social networks is provided where by introducing the concept of dependency, it is tried to accelerate the process of extracting conceptual links. Although the proposed algorithm will be able to accelerate this process if there are dependencies between data, but the tests carried out on Pokec social network, which lacks dependency between its data, revealed that absence of dependency, increases execution time of extracting conceptual links only up to 15 percent.

show abstract

Clustering of Links and Clustering of Nodes: Fusion of Knowledge in Social Networks

Stattner¹,

Collard²

2016

Advances in Knowledge Discovery and Management

View full text Add to dashboard Cite

Towards a hybrid algorithm for extracting maximal frequent conceptual links in social networks

Cited by 6 publications

References 14 publications

Offering a New Approach for Extracting Recurring Conceptual Links from Social Networks

Offering a New Approach for Extracting Recurring Conceptual Links from Social Networks

D-MFCLMin: A New Algorithm for Extracting Frequent Conceptual Links from Social Networks

Clustering of Links and Clustering of Nodes: Fusion of Knowledge in Social Networks

Contact Info

Product

Resources

About