Estimating the dynamics of kernel-based evolving networks

Csárdi, Gábor; Strandburg, Katherine J.; Zalányi, László; Tobochnik, Jan; Érdi, Péter

doi:10.1007/978-3-540-85081-6_12

Cited by 47 publications

(62 citation statements)

References 19 publications

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“…We test the method against the spectral method maximizing Q und and Q dir and the so-called Louvain method, which is a nonspectral algorithm that heuristically maximizes Q und [51]. We use the implementation of the Louvain method in the igraph package of R [52]. In our implementation of the spectral method, we add a fine tuning step after every bipartitioning in a similar fashion to references [48,49] (Appendix A).…”

Section: Resultsmentioning

confidence: 99%

Community detection in directed acyclic graphs

2015

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Abstract. Some temporal networks, most notably citation networks, are naturally represented as directed acyclic graphs (DAGs). To detect communities in DAGs, we propose a modularity for DAGs by defining an appropriate null model (i.e., randomized network) respecting the order of nodes. We implement a spectral method to approximately maximize the proposed modularity measure and test the method on citation networks and other DAGs. We find that the attained values of the modularity for DAGs are similar for partitions that we obtain by maximizing the proposed modularity (designed for DAGs), the modularity for undirected networks and that for general directed networks. In other words, if we neglect the order imposed on nodes (and the direction of links) in a given DAG and maximize the conventional modularity measure, the obtained partition is close to the optimal one in the sense of the modularity for DAGs.

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Section: Resultsmentioning

confidence: 99%

Community detection in directed acyclic graphs

2015

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“…All analyses described here have been carried out using the open-source statistical software R [11]. The igraph package has been utilized to carry out analyses of the network structures [12]. In order to explore potential effects of the physical classroom on student collaboration, we will visualize the networks, and compare their network properties, and in order to reconstruct groups, we will utilize community detection algorithms developed by the statistical physics and applied mathematics communities [13].…”

Section: Classes As Social Networkmentioning

confidence: 99%

Group Formation on Physics Exams

Wolf¹,

Blakeney²,

Close³

2016

2016 Physics Education Research Conference Proceedings

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As our classrooms become more active and collaborative, we need to consider ways that our assessments can take on the same active and collaborative spirit that our classes have. One way that we can accomplish this goal is through the use of group exams. In exams at Texas State University and East Carolina University, we have utilized a two-phase exam format allowing open collaboration-that is, allowing students to form their own groups. We have analyzed self-reported collaboration data and find that the room influences how students will associate with each other. We have also compared collaboration patterns evident in student responses for students in open collaboration settings to students in closed collaboration settings.

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“…The igraph library was used to generate and modify the graphs [22]. Figure 2 shows the shortest path variation in flooding communication; from left to right, node addition and node removal.…”

Section: Perturbationsmentioning

confidence: 99%

Structural changes in data communication in wireless sensor networks

et al. 2013

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Abstract:Wireless sensor networks are an important technology for making distributed autonomous measures in hostile or inaccessible environments. Among the challenges they pose, the way data travel among them is a relevant issue since their structure is quite dynamic. The operational topology of such devices can often be described by complex networks. In this work, we assess the variation of measures commonly employed in the complex networks literature applied to wireless sensor networks. Four data communication strategies were considered: geometric, random, small-world, and scale-free models, along with the shortest path length measure. The sensitivity of this measure was analyzed with respect to the following perturbations: insertion and removal of nodes in the geometric strategy; and insertion, removal and rewiring of links in the other models. The assessment was performed using the normalized Kullback-Leibler divergence and Hellinger distance quantifiers, both deriving from the Information Theory framework. The results reveal that the shortest path length is sensitive to perturbations. PACS

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Estimating the dynamics of kernel-based evolving networks

Cited by 47 publications

References 19 publications

Community detection in directed acyclic graphs

Community detection in directed acyclic graphs

Group Formation on Physics Exams

Structural changes in data communication in wireless sensor networks

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