Accelerating the Kamada-Kawai Algorithm for Boundary Detection in a Mobile Ad Hoc Network

Cheong, Se-Hang; Si, Yain-Whar

doi:10.1145/3005718

Cited by 11 publications

(9 citation statements)

References 23 publications

(34 reference statements)

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“…Data quality analysis is an effort to obtain data quality by measuring objectively [25]. Failure to produce good quality data at the pre-processing stage will significantly reduce the accuracy of each data analysis job.…”

Section: Modeling Analysismentioning

confidence: 99%

Student Performance Based on Activity Log on Social Network and e-Learning

Agusriandi¹,

Sitanggang²,

Wijaya³

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Learning activities in social networks and e-learning platforms bring massive activity log in the database, making it challenging to measure students’ performance. Data mining technique and social network analysis provide some benefits in the field of education in discovering knowledge from hidden information of student’s activities on e-learning and social network environment. This study aims to identify dominant students on social network group based on centrality values and to analyze log data from the activities on e-learning using process mining technique. Centrality value was measured by analyzing data quality or data pre-processing, creating the network, measuring the network, and highlighting degrees and layouts. The process mining technique included data pre-processing, discovering process, and conformance checking. This study found that dominant students were identified from a high hub score and authority. This study also found a free-rider student. The presence of dominant students and free-riders made the collaboration of social network group are weak. This study also found that student performance on e-learning has been discovered where the student’s activity, namely, the course module viewed and course viewed, were more frequent than other activities. On the other hand, an optimum fitness value was obtained, i.e., 0.94 on all the processes of e-learning. This study provides insights that can be used to improve student collaboration and to enhance online learning activities.

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Section: Modeling Analysismentioning

confidence: 99%

Student Performance Based on Activity Log on Social Network and e-Learning

Agusriandi¹,

Sitanggang²,

Wijaya³

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…We take the full skill × skill matrix of interdependence values to be the adjacency matrix of a complete and weighted national-level interdependence network, in which nodes are the various labor skills and the weights are pair-wise interdependence values (Figure 1). Networks were created using the Kamada-Kawai algorithm, which produces evenly spaced nodes for relatively small networks and minimizes the number of edge crossings [25,26]. Because interdependence captures the degree to which skills tend to co-occur across MSAs, two skills that are highly interdependent tend to be near to each other in our network, while pairs with a low or negative interdependence tend to be farther apart.…”

Section: Skills and Interdependencementioning

confidence: 99%

Inferring Networks of Interdependent Labor Skills to Illuminate Urban Economic Structure

Shutters

Waters

2020

SSRN Journal

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“…Extensions of the FR algorithm were proposed for node placement by Wang et al 208 and Ma et al 209 There are also extensions based on a modified FR algorithm that estimate the approximate location of each node based on signal information. 210,211 Cheong and Si 212 proposed a heuristic KK algorithm for boundary detection. The proposed algorithm was optimised for sending emergency messages via mobile ad hoc network if cellular networks are corrupted.…”

Section: Applications Of Force-directed Algorithmsmentioning

confidence: 99%

Force-directed algorithms for schematic drawings and placement: A survey

Cheong

2019

Information Visualization

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Force-directed algorithms have been developed over the last 50 years and used in many application fields, including information visualisation, biological network visualisation, sensor networks, routing algorithms, scheduling, and graph drawing. Our survey provides a comprehensive summary of developments and a full roadmap for state-of-the-art force-directed algorithms in schematic drawings and placement. We classified the model of force-directed algorithms into classical and hybrid. The classical force-directed algorithms are further classified as follows: (a) accumulated force models, (b) energy function minimisation models and (c) combinatorial optimisation models. The hybrid force-directed algorithms are classified as follows: (a) parallel and hardware accelerated models, (b) multilevel force-directed models and (c) multidimensional scaling force-directed algorithms. Five categories of application domains in which force-directed algorithms have been adopted for schematic drawings and placement are also summarised: (a) aesthetic drawings for general networks, (b) component placement and scheduling in high-level synthesis of very-large-scale integration circuits design, (c) information visualisation, (d) biological network visualisation and (e) node placement and localisation for sensor networks.

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Accelerating the Kamada-Kawai Algorithm for Boundary Detection in a Mobile Ad Hoc Network

Cited by 11 publications

References 23 publications

Student Performance Based on Activity Log on Social Network and e-Learning

Student Performance Based on Activity Log on Social Network and e-Learning

Inferring Networks of Interdependent Labor Skills to Illuminate Urban Economic Structure

Force-directed algorithms for schematic drawings and placement: A survey

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