Identifying influential segments from word co-occurrence networks using AHP

Garg, Muskan; Kumar, Mukesh

doi:10.1016/j.cogsys.2017.07.003

Cited by 23 publications

(14 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…( 2009 ), and Microblogs Garg and Kumar ( 2018a ) to name a few. Recently, in the study of the structure of WCN for Microblogs Garg and Kumar ( 2018b ), the network properties are observed by increasing the number of Microblog in the dataset from 100 to 100k. It is observed that the number of edges increases at higher rate than that of the number of nodes.…”

Section: Stability Of the Structure Of The Graph Of Wordsmentioning

confidence: 99%

See 1 more Smart Citation

A survey on different dimensions for graphical keyword extraction techniques

Garg

2021

Artif Intell Rev

Self Cite

View full text Add to dashboard Cite

Section: Stability Of the Structure Of The Graph Of Wordsmentioning

confidence: 99%

“…It is observed that calculation of eigenvalues for GoW can help in dimensionality reduction in spectral clustering Garg and Kumar ( 2018b ) and in identifying tight concentrations in random walk graph Bougouin et al. ( 2013 ) .…”

Section: Diversity In Approaches Over Graph Of Words For Akementioning

confidence: 99%

A survey on different dimensions for graphical keyword extraction techniques

Garg

2021

Artif Intell Rev

Self Cite

View full text Add to dashboard Cite

“…For extracting engaged keywords, several methods such as leveraging Co-Occurrence network analysis [4,5] or Word2Vec [6][7][8] are already proposed. Even after applying these methods, there is still an issue of how to select actually used keywords from obtained keywords.…”

Section: Introductionmentioning

confidence: 99%

Properly initialized Bayesian Network for decision making leveraging random forest

Iwakami

Takuma²,

Iwashita³

2020

AIR

View full text Add to dashboard Cite

Bayesian network is one of major methods for probabilistic inference among items. But if it contains particular targeting node and other explanatory nodes for decision making, for example how to select suitable appealing keywords to make customers like a product, edges around the target should be counted with more importance than those among others while constructing the network. In order to achieve this adjustment, this study proposes to configure initial state consisting of a few nodes and their edges connected with the target. The initial state is obtained by leveraging Random forest which is a proven method for decision making. Initial nodes are extracted by measuring mean decrease of Gini coefficient calculated with decision trees of Random forest. Initial edges are designated by comparing Kullback-Leibler divergences of conditional probability distribution among nodes which are corresponding to edge directions. Through an actual experiment, this method is confirmed to be effective for adjusting Bayesian network in decision making. This approach is especially useful for business scenes, such as selecting preferable keywords for appealing products over SNS.

show abstract

“…Co-occurrence networks capture relationships between words appearing in the same unit of text: each node is a word, or a group of words, and an edge is defined between two nodes if they appear in the same unit of text. Co-occurrence networks have been used, among other things, to study the structure of human languages [11], to detect influential text segments [12] and to identify authorship signature in temporal evolving networks [1]. Other applications include the study of co-citations of patents [37], articles [18] and genes [16,21].…”

Section: Introductionmentioning

confidence: 99%

Co-occurrence simplicial complexes in mathematics: identifying the holes of knowledge

et al. 2018

View full text Add to dashboard Cite

In the last years complex networks tools contributed to provide insights on the structure of research, through the study of collaboration, citation and co-occurrence networks. The network approach focuses on pairwise relationships, often compressing multidimensional data structures and inevitably losing information. In this paper we propose for the first time a simplicial complex approach to word co-occurrences, providing a natural framework for the study of higher-order relations in the space of scientific knowledge. Using topological methods we explore the conceptual landscape of mathematical research, focusing on homological holes, regions with low connectivity in the simplicial structure. We find that homological holes are ubiquitous, which suggests that they capture some essential feature of research practice in mathematics. k-dimensional holes die when every concept in the hole appears in an article together with other k+1 concepts in the hole, hence their death may be a sign of the creation of new knowledge, as we show with some examples. We find a positive relation between the size of a hole and the time it takes to be closed: larger holes may represent potential for important advances in the field because they separate conceptually distant areas. We provide further description of the conceptual space by looking for the simplicial analogs of stars and explore the likelihood of edges in a star to be also part of a homological cycle. We also show that authors’ conceptual entropy is positively related with their contribution to homological holes, suggesting that polymaths tend to be on the frontier of research.

show abstract

Identifying influential segments from word co-occurrence networks using AHP

Cited by 23 publications

References 14 publications

A survey on different dimensions for graphical keyword extraction techniques

A survey on different dimensions for graphical keyword extraction techniques

Properly initialized Bayesian Network for decision making leveraging random forest

Co-occurrence simplicial complexes in mathematics: identifying the holes of knowledge

Contact Info

Product

Resources

About