A Qualitative Analysis on Force Directed Network Visualization Tools in the Context of Large Complex Networks

Broască, Laura; Ancusa, Versavia-Maria; Ciocârlie, Horia

doi:10.1109/icstcc.2019.8885641

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…For all the features, the distance measure is used to quantify the discrepancies between sequences in which the proximity value is obtained based on the threshold value, compared to one for all user profiles. Connectivity is defined using proximity values and the network model is constructed using the NetworkX [42]open-source framework, a graphbased platform for mining, that easily models the construction of social networks, as shown in figure 2.…”

Section: Resultsmentioning

confidence: 99%

Connecting User Profiles of Social Networks Using Proximity-Based Clustering

Kodabagi

2022

MJCS

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The establishment of connections among social network users using their profile information is an important task in social network analysis, which facilitates the development of various technological solutions such as stock market analysis, crime detection, tracking system of fraudulent events, etc. In this work, a proximity-based clustering method for networking LinkedIn profiles is presented. The proposed system computes proximity value between users using various attributes of user profiles. The proximity measures are computed by analyzing unstructured data of user profiles to connect users. The method addresses various issues such as comparison of familiar sentences, finding patterns, and sub-patterns among user profiles, assigning weights on attributes similarity, and computing total similarity which is associated with unstructured data. After computing proximity measures on various attributes of user profiles, the connecting edges between nodes are determined by employing artificial intelligence and a network graph is formed. The method is evaluated on a LinkedIn data-set to form a connected graph. The strength of the proposed methodology lies in the formation of multi-layered network graphs, as it uses various attributes of the user profiles to connect them. The proposed methodology helps various applications like recommendation systems to form network graphs of selected attributes and perform the social network analysis. The method achieves an accuracy of 96%. However, the profiles containing abbreviations of important information are not matched and the system accuracy drops down in such cases.

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Section: Resultsmentioning

confidence: 99%

Connecting User Profiles of Social Networks Using Proximity-Based Clustering

Kodabagi

2022

MJCS

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“…The focus is on minimizing deviations and maximizing readability. The algorithm increases the attraction between highly connected nodes while repulsing unrelated nodes [66]. Repulsion focuses on dissimilarities, describing how strongly nodes reject each other, while attraction points out similarities [67].…”

Section: Simultaneously It Illustrates Interrelationships and Depende...mentioning

confidence: 99%

Determining Critical Success Factors of the Digital Transformation Using a Force-Directed Network Graph

Pasqual,

Jung,

Fraunholz

2023

J. Complex Syst. Inform. Model. Q.

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Conducting a digital transformation is one of the major challenges for today’s companies as it is usually associated with a high risk. The reasons for this are manifold. Technologies are still evolving and there is no coherent standard for digital platforms enabling digitalization. Furthermore, it is not only about introducing new technology but also requires a fundamental change in an organization and its culture. Consequently, planning a digital transformation project (or program) requires a careful analysis of the company’s current situation and the envisioned objectives. This article investigates critical success factors for preparing and executing such a transformation. The success factors are identified by conducting a structured literature analysis. 13 scientific papers are identified and then analyzed quantitatively and qualitatively. The quantitative analysis is conducted by using force-directed network graphs. Both methods are then compared and discussed. The result shows that most critical success factors are related to the business change rather than to introducing technology. Leadership, strategy, vision, corporate culture, and customer centricity play a stronger role than a digital platform.

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