Patterns of Interactions in Complex Social Networks Based on Coloured Motifs Analysis

Coloured network motifs are small subgraphs that enable to discover and interpret the patterns of interaction within the complex networks. The analysis of three-nodes motifs where the colour of the node reflects its high – white node or low – black node centrality in the social network is presented in the paper. The importance of the vertices is assessed by utilizing two measures: degree prestige and degree centrality. The distribution of motifs in these two cases is compared to mine the interconnection patterns between nodes. The analysis is performed on the social network derived from email communication

Temporal Changes in Local Topology of an Email-Based Social Network

The dynamics of complex social networks has become one of the research areas of growing importance. The knowledge about temporal changes of the network topology and characteristics is crucial in networked communication systems in which accurate predictions are important. The local network topology can be described by the means of network motifs which are small subgraphs -- usually containing from 3 to 7 nodes. They were shown to be useful for creating profiles that reveal several properties of the network. In this paper, the time-varying characteristics of social networks, such as the number of nodes and edges as well as clustering coefficients and different centrality measures are investigated. At the same time, the analysis of three-node motifs (triads) was used to track the temporal changes in the structure of a large social network derived from e-mail communication between university employees. We have shown that temporal changes in local connection patterns of the social network are indeed correlated with the changes in the clustering coefficient as well as various centrality measures values and are detectable by means of motifs analysis. Together with robust sampling network motifs can provide an appealing way to monitor and assess temporal changes in large social networks

An Event-based Analysis Framework for Open Source Software Development Projects

The increasing popularity and success of Open Source Software (OSS) development projects has drawn significant attention of academics and open source participants over the last two decades. As one of the key areas in OSS research, assessing and predicting OSS performance is of great value to both OSS communities and organizations who are interested in investing in OSS projects. Most existing research, however, has considered OSS project performance as the outcome of static cross-sectional factors such as number of developers, project activity level, and license choice. While variance studies can identify some predictors of project outcomes, they tend to neglect the actual process of development. Without a closer examination of how events occur, an understanding of OSS projects is incomplete. This dissertation aims to combine both process and variance strategy, to investigate how OSS projects change over time through their development processes; and to explore how these changes affect project performance. I design, instantiate, and evaluate a framework and an artifact, EventMiner, to analyze OSS projects’ evolution through development activities. This framework integrates concepts from various theories such as distributed cognition (DCog) and complexity theory, applying data mining techniques such as decision trees, motif analysis, and hidden Markov modeling to automatically analyze and interpret the trace data of 103 OSS projects from an open source repository. The results support the construction of process theories on OSS development. The study contributes to literature in DCog, design routines, OSS development, and OSS performance. The resulting framework allows OSS researchers who are interested in OSS development processes to share and reuse data and data analysis processes in an open-source manner

Higher-order Network Analysis of Fine Particulate Matter (PM 2.5) Transport in China at City Level

abstract: Specification of PM[subscript 2.5] transmission characteristics is important for pollution control and policymaking. We apply higher-order organization of complex networks to identify major potential PM[subscript 2.5] contributors and PM[subscript 2.5] transport pathways of a network of 189 cities in China. The network we create in this paper consists of major cities in China and contains information on meteorological conditions of wind speed and wind direction, data on geographic distance, mountains, and PM[subscript 2.5] concentrations. We aim to reveal PM[subscript 2.5] mobility between cities in China. Two major conclusions are revealed through motif analysis of complex networks. First, major potential PM[subscript 2.5] pollution contributors are identified for each cluster by one motif, which reflects movements from source to target. Second, transport pathways of PM[subscript 2.5] are revealed by another motif, which reflects transmission routes. To our knowledge, this is the first work to apply higher-order network analysis to study PM[subscript 2.5] transport.The final version of this article, as published in Scientific Reports, can be viewed online at: http://www.nature.com/articles/s41598-017-13614-

TLP-NEGCN: Temporal Link Prediction via Network Embedding and Graph Convolutional Networks

Temporal link prediction (TLP) is a prominent problem in network analysis that focuses on predicting the existence of future connections or relationships between entities in a dynamic network over time. The predictive capabilities of existing models of TLP are often constrained due to their difficulty in adapting to the changes in dynamic network structures over time. In this article, an improved TLP model, denoted as TLP-NEGCN, is introduced by leveraging network embedding, graph convolutional networks (GCNs), and bidirectional long short-term memory (BiLSTM). This integration provides a robust model of TLP that leverages historical network structures and captures temporal dynamics leading to improved performances. We employ graph embedding with self-clustering (GEMSEC) to create lower dimensional vector representations for all nodes of the network at the initial timestamps. The node embeddings are fed into an iterative training process using GCNs across timestamps in the dataset. This process enhances the node embeddings by capturing the network’s temporal dynamics and integrating neighborhood information. We obtain edge embeddings by concatenating the node embeddings of the end nodes of each edge, encapsulating the information about the relationships between nodes in the network. Subsequently, these edge embeddings are processed through a BiLSTM architecture to forecast upcoming links in the network. The performance of the proposed model is compared against several baselines and contemporary TLP models on various real-life temporal datasets. The obtained results based on various evaluation metrics demonstrate the superiority of the proposed work

Beyond hairballs: depicting complexity of a kinase-phosphatase network in the budding yeast

Les kinases et les phosphatases (KP) représentent la plus grande famille des enzymes dans la cellule. Elles régulent les unes les autres ainsi que 60 % du protéome, formant des réseaux complexes kinase-phosphatase (KP-Net) jouant un rôle essentiel dans la signalisation cellulaire. Ces réseaux caractérisés d’une organisation de type commandes-exécutions possèdent généralement une structure hiérarchique. Malgré les nombreuse études effectuées sur le réseau KP-Net chez la levure, la structure hiérarchique ainsi que les principes fonctionnels sont toujours peux connu pour ce réseau. Dans ce contexte, le but de cette thèse consistait à effectuer une analyse d’intégration des données provenant de différentes sources avec la structure hiérarchique d’un réseau KP-Net de haute qualité chez la levure, S. cerevisiae, afin de générer des hypothèses concernant les principes fonctionnels de chaque couche de la hiérarchie du réseau KP-Net. En se basant sur une curation de données d’interactions effectuée dans la présente et dans d’autres études, le plus grand et authentique réseau KP-Net reconnu jusqu’à ce jour chez la levure a été assemblé dans cette étude. En évaluant le niveau hiérarchique du KP-Net en utilisant la métrique de la centralisation globale et en élucidant sa structure hiérarchique en utilisant l'algorithme vertex-sort (VS), nous avons trouvé que le réseau KP-Net possède une structure hiérarchique ayant la forme d’un sablier, formée de trois niveaux disjoints (supérieur, central et inférieur). En effet, le niveau supérieur du réseau, contenant un nombre élevé de KPs, était enrichi par des KPs associées à la régulation des signaux cellulaire; le niveau central, formé d’un nombre limité de KPs fortement connectées les unes aux autres, était enrichi en KPs impliquées dans la régulation du cycle cellulaire; et le niveau inférieur, composé d’un nombre important de KPs, était enrichi en KPs impliquées dans des processus cellulaires diversifiés. En superposant une grande multitude de propriétés biologiques des KPs sur le réseau KP-Net, le niveau supérieur était enrichi en phosphatases alors que le niveau inférieur en était appauvri, suggérant que les phosphatases seraient moins régulées par phosphorylation et déphosphorylation que les kinases. De plus, le niveau central était enrichi en KPs représentant des « bottlenecks », participant à plus d’une voie de signalisation, codées par des gènes essentiels et en KPs qui étaient les plus strictement régulées dans l’espace et dans le temps. Ceci implique que les KPs qui jouent un rôle essentiel dans le réseau KP-Net devraient être étroitement contrôlées. En outre, cette étude a montré que les protéines des KPs classées au niveau supérieur du réseau sont exprimées à des niveaux d’abondance plus élevés et à un niveau de bruit moins élevé que celles classées au niveau inférieur du réseau, suggérant que l’expression des enzymes à des abondances élevées invariables au niveau supérieur du réseau KP-Net pourrait être importante pour assurer un système robuste de signalisation. L’étude de l’algorithme VS a montré que le degré des nœuds affecte leur classement dans les différents niveaux d’un réseau hiérarchique sans biaiser les résultats biologiques du réseau étudié. En outre, une analyse de robustesse du réseau KP-Net a montré que les niveaus du réseau KP-Net sont modérément stable dans des réseaux bruités générés par ajout d’arrêtes au réseau KP-Net. Cependant, les niveaux de ces réseaux bruités et de ceux du réseau KP-Net se superposent significativement. De plus, les propriétés topologiques et biologiques du réseau KP-Net étaient retenues dans les réseaux bruités à différents niveaux. Ces résultats indiquant que bien qu’une robustesse partielle de nos résultats ait été observée, ces derniers représentent l’état actuel de nos connaissances des réseaux KP-Nets. Finalement, l’amélioration des techniques dédiées à l’identification des substrats des KPs aideront davantage à comprendre comment les réseaux KP-Nets fonctionnent. À titre d’exemple, je décris, dans cette thèse, une stratégie que nous avons conçu et qui permet à déterminer les interactions KP-substrats et les sous-unités régulatrices sur lesquelles ces interactions dépendent. Cette stratégie est basée sur la complémentation des fragments de protéines basée sur la cytosine désaminase chez la levure (OyCD PCA). L’OyCD PCA représente un essai in vivo à haut débit qui promet une description plus précise des réseaux KP-Nets complexes. En l’appliquant pour déterminer les substrats de la kinase cycline-dépendante de type 1 (Cdk1, appelée aussi Cdc28) chez la levure et l’implication des cyclines dans la phosphorylation de ces substrats par Cdk1, l’essai OyCD PCA a montré un comportement compensatoire collectif des cyclines pour la majorité des substrats. De plus, cet essai a montré que la tubuline- γ est phosphorylée spécifiquement par Clb3-Cdk1, établissant ainsi le moment pendant lequel cet événement contrôle l'assemblage du fuseau mitotique.Kinases and phosphatases (KP) form the largest family of enzymes in living cells. They regulate each other and 60 % of the proteome forming complex kinase-phosphatase networks (KP-Net) essential for cell signaling. Such networks having the command-execution aspect tend to have a hierarchical structure. Despite the extensive study of the KP-Net in the budding yeast, the hierarchical structure as well as the functional principles of this network are still not known. In this context, this thesis aims to perform an integrative analysis of multi-omics data with the hierarchical structure of a bona fide KP-Net in the budding yeast Saccharomyces cerevisiae, in order to generate hypotheses about the functional principles of each layer in the KP-Net hierarchy. Based on a literature curation effort accomplished in this and in other studies, the largest bona fide KP-Net of the S. cerevisiae known to date was assembled in this thesis. By assessing the hierarchical level of the KP-Net using the global reaching centrality and by elucidating the its hierarchical structure using the vertex-sort (VS) algorithm, we found that the KP-Net has a moderate hierarchical structure made of three disjoint layers (top, core and bottom) resembling a bow tie shape. The top layer having a large size was found enriched for signaling regulation; the core layer made of few strongly connected KPs was found enriched mostly for cell cycle regulation; and the bottom layer having a large size was found enriched for diverse biological processes. On overlaying a wide range of KP biological properties on top of the KP-Net hierarchical structure, the top layer was found enriched for and the bottom layer was found depleted for phosphatases, suggesting that phosphatases are less regulated by phosphorylation and dephosphoryation interactions (PDI) than kinases. Moreover, the core layer was found enriched for KPs representing bottlenecks, pathway-shared components, essential genes and for the most tightly regulated KPs in time and space, implying that KPs playing an essential role in the KP-Net should be firmly controlled. Interestingly, KP proteins in the top layer were found more abundant and less noisy than those of the bottom layer, suggesting that availability of enzymes at invariable protein expression level at the top of the network might be important to ensure a robust signaling. Analysis of the VS algorithm showed that node degrees affect their classification in the different layers of a network hierarchical structure without biasing biological results of the sorted network. Robustness analysis of the KP-Net showed that KP-Net layers are moderately stable in noisy networks generated by adding edges to the KP-Net. However, layers of these noisy overlap significantly with those of the KP-Net. Moreover, topological and biological properties of the KP-Net were retained in the noisy networks to different levels. These findings indicate that despite the observed partial robustness of our results, they mostly represent our current knowledge about KP-Nets. Finally, enhancement of techniques dedicated to identify KPs substrates will enhance our understanding about how KP-Nets function. As an example, I describe here a strategy that we devised to help in determining KP-substrate interactions and the regulatory subunits on which these interactions depend. The strategy is based on a protein-fragment complementation assay based on the optimized yeast cytosine deaminase (OyCD PCA). The OyCD PCA represents a large scale in vivo screen that promises a substantial improvement in delineating the complex KP-Nets. We applied the strategy to determine substrates of the cyclin-dependent kinase 1 (Cdk1; also called Cdc28) and cyclins implicated in phosphorylation of these substrates by Cdk1 in S. cerevisiae. The OyCD PCA showed a wide compensatory behavior of cyclins for most of the substrates and the phosphorylation of γ-tubulin specifically by Clb3-Cdk1, thus establishing the timing of the latter event in controlling assembly of the mitotic spindle

Social Network Analysis Based on Network Motifs

Based on the community structure characteristics, theory, and methods of frequent subgraph mining, network motifs findings are firstly introduced into social network analysis; the tendentiousness evaluation function and the importance evaluation function are proposed for effectiveness assessment. Compared with the traditional way based on nodes centrality degree, the new approach can be used to analyze the properties of social network more fully and judge the roles of the nodes effectively. In application analysis, our approach is shown to be effective

1996 NASA-Hampton University American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program

NASA has supported a program of summer faculty fellowships for engineering and science educators. In a series of collaborations between NASA research and development centers and nearby universities, engineering faculty members spend 10 weeks working with professional peers on research. The Summer Faculty Program Committee of the American Society for Engineering Education supervises the programs. The objectives were: (1) To further the professional knowledge of qualified engineering and science faculty members; (2) To stimulate and exchange ideas between participants and NASA; (3) To enrich and refresh the research and teaching activities of participants institutions; (4) To contribute to the research objectives of the NASA Center. Program Description: College or university faculty members will be appointed as Research Fellows to spend 10 weeks in cooperative research and study at the NASA Langley Research Center. The Fellow will devote approximately 90 percent of the time to a research problem and the remaining time to a study program. The study program will consist of lectures and seminars on topics of interest or that are directly relevant to the Fellows' research topics. The lectures and seminar leaders will be distinguished scientists and engineers from NASA, education, or industry

Transition UGent: a bottom-up initiative towards a more sustainable university

The vibrant think-tank ‘Transition UGent’ engaged over 250 academics, students and people from the university management in suggesting objectives and actions for the Sustainability Policy of Ghent University (Belgium). Founded in 2012, this bottom-up initiative succeeded to place sustainability high on the policy agenda of our university. Through discussions within 9 working groups and using the transition management method, Transition UGent developed system analyses, sustainability visions and transition paths on 9 fields of Ghent University: mobility, energy, food, waste, nature and green, water, art, education and research. At the moment, many visions and ideas find their way into concrete actions and policies. In our presentation we focused on the broad participative process, on the most remarkable structural results (e.g. a formal and ambitious Sustainability Vision and a student-led Sustainability Office) and on recent actions and experiments (e.g. a sustainability assessment on food supply in student restaurants, artistic COP21 activities, ambitious mobility plans, food leftovers projects, an education network on sustainability controversies, a transdisciplinary platform on Sustainable Cities). We concluded with some recommendations and reflections on this transition approach, on the important role of ‘policy entrepreneurs’ and student involvement, on lock-ins and bottlenecks, and on convincing skeptical leaders