Prominence and Control: The Weighted Rich-Club Effect

Published in Physical Review Letters PRL 101, 168702 (2008)http://link.aps.org/doi/10.1103/PhysRevLett.101.168702. Copyright American Physical Society (APS).Publisher's note: Erratum in Phys Rev Lett. 2008 Oct 31;101(18):189903 http://link.aps.org/doi/10.1103/PhysRevLett.101.18990

Revisiting the small-world phenomenon: efficiency variation and classification of small-world networks

Research has explored how embeddedness in small-world networks influences individual and firm outcomes. We show that there remains significant heterogeneity among networks classified as small-world networks. We develop measures of the efficiency of a network, which allow us to refine predictions associated with small-world networks. A network is classified as a small-world network if it exhibits a distance between nodes that is comparable to the distance found in random networks of similar sizes—with ties randomly allocated among nodes—in addition to containing dense clusters. To assess how efficient a network is, there are two questions worth asking: (i) ‘what is a compelling random network for baseline levels of distance and clustering?’ and (ii) ‘how proximal should an observed value be to the baseline to be deemed comparable?’. Our framework tests properties of networks, using simulation, to further classify small-world networks according to their efficiency. Our results suggest that small-world networks exhibit significant variation in efficiency. We explore implications for the field of management and organization

Estimating and controlling the traffic Impact of a collaborative P2P system

Nowadays, P2P applications are commonly used in the Internet being an important paradigm for the development of distinct services. However, the dissemination of P2P applications also entails some important challenges that should be carefully addressed. In particular, some of the important coexistence problems existing between P2P applications and Internet Service Providers (ISPs) are mainly motivated by the inherent P2P dynamics which cause traffic to scatter across the network links in an unforeseeable way. In this context, this work proposes a collaborative framework of a Bit- Torrent like system. Using the proposed framework and based on the exchange of valuable information between the application and network levels, some novel techniques are proposed allowing to estimate and control the traffic impact that the P2P system will have on the links of the underlying network infrastructure. Both the framework and the presented techniques were tested resorting to simulation. The results clearly corroborate the viability and effectiveness of the formulated methods

A New Methodology for Generalizing Unweighted Network Measures

Several important complex network measures that helped discovering common patterns across real-world networks ignore edge weights, an important information in real-world networks. We propose a new methodology for generalizing measures of unweighted networks through a generalization of the cardinality concept of a set of weights. The key observation here is that many measures of unweighted networks use the cardinality (the size) of some subset of edges in their computation. For example, the node degree is the number of edges incident to a node. We define the effective cardinality, a new metric that quantifies how many edges are effectively being used, assuming that an edge's weight reflects the amount of interaction across that edge. We prove that a generalized measure, using our method, reduces to the original unweighted measure if there is no disparity between weights, which ensures that the laws that govern the original unweighted measure will also govern the generalized measure when the weights are equal. We also prove that our generalization ensures a partial ordering (among sets of weighted edges) that is consistent with the original unweighted measure, unlike previously developed generalizations. We illustrate the applicability of our method by generalizing four unweighted network measures. As a case study, we analyze four real-world weighted networks using our generalized degree and clustering coefficient. The analysis shows that the generalized degree distribution is consistent with the power-law hypothesis but with steeper decline and that there is a common pattern governing the ratio between the generalized degree and the traditional degree. The analysis also shows that nodes with more uniform weights tend to cluster with nodes that also have more uniform weights among themselves.Comment: 23 pages, 10 figure

Wastewater characterisation by combining size fractionation, chemical composition and biodegradability

The potential for resource recovery from wastewater can be evaluated based on a detailed characterisation of wastewater. In this paper, results from fractionation and characterisation of two distinct wastewaters are reported. Using tangential flow filtration, the wastewater was fractionated into 10 size fractions ranging from 1 kDa to 1 mm, wherein the chemical composition and biodegradability were determined. Carbohydrates were dominant in particulate size fractions larger than 100 μm, indicating a potential of cellulose recovery from these fractions. While the particulate size fractions between 0.65 and 100 μm show a potential as a source for biofuel production due to an abundance of saturated C16 and C18 lipids. Both wastewaters were dominated by particulate (>0.65 μm), and oligo- and monomeric (<1 kDa) COD. Polymeric (1–1000 kDa) and colloidal (1000 kDa-0.65 μm) fractions had a low COD content, expected due to degradation in the sewer system upstream of the wastewater treatment plant. Biodegradation rates of particulate fractions increase with decreasing size. However, this was not seen in polymeric fractions where degradation rate was governed by chemical composition. Analytical validation of molecular weight and particle size distribution showed below filter cut-off retention of particles and polymers close to nominal cut-off, shifting the actual size distribution

Seeds Buffering for Information Spreading Processes

Seeding strategies for influence maximization in social networks have been studied for more than a decade. They have mainly relied on the activation of all resources (seeds) simultaneously in the beginning; yet, it has been shown that sequential seeding strategies are commonly better. This research focuses on studying sequential seeding with buffering, which is an extension to basic sequential seeding concept. The proposed method avoids choosing nodes that will be activated through the natural diffusion process, which is leading to better use of the budget for activating seed nodes in the social influence process. This approach was compared with sequential seeding without buffering and single stage seeding. The results on both real and artificial social networks confirm that the buffer-based consecutive seeding is a good trade-off between the final coverage and the time to reach it. It performs significantly better than its rivals for a fixed budget. The gain is obtained by dynamic rankings and the ability to detect network areas with nodes that are not yet activated and have high potential of activating their neighbours.Comment: Jankowski, J., Br\'odka, P., Michalski, R., & Kazienko, P. (2017, September). Seeds Buffering for Information Spreading Processes. In International Conference on Social Informatics (pp. 628-641). Springe

Clones in Graphs

Finding structural similarities in graph data, like social networks, is a far-ranging task in data mining and knowledge discovery. A (conceptually) simple reduction would be to compute the automorphism group of a graph. However, this approach is ineffective in data mining since real world data does not exhibit enough structural regularity. Here we step in with a novel approach based on mappings that preserve the maximal cliques. For this we exploit the well known correspondence between bipartite graphs and the data structure formal context $(G,M,I)$ from Formal Concept Analysis. From there we utilize the notion of clone items. The investigation of these is still an open problem to which we add new insights with this work. Furthermore, we produce a substantial experimental investigation of real world data. We conclude with demonstrating the generalization of clone items to permutations.Comment: 11 pages, 2 figures, 1 tabl

Global clustering coefficient in scale-free networks

In this paper, we analyze the behavior of the global clustering coefficient in scale free graphs. We are especially interested in the case of degree distribution with an infinite variance, since such degree distribution is usually observed in real-world networks of diverse nature. There are two common definitions of the clustering coefficient of a graph: global clustering and average local clustering. It is widely believed that in real networks both clustering coefficients tend to some positive constant as the networks grow. There are several models for which the average local clustering coefficient tends to a positive constant. On the other hand, there are no models of scale-free networks with an infinite variance of degree distribution and with a constant global clustering. In this paper we prove that if the degree distribution obeys the power law with an infinite variance, then the global clustering coefficient tends to zero with high probability as the size of a graph grows

The Parameterized Complexity of Centrality Improvement in Networks

The centrality of a vertex v in a network intuitively captures how important v is for communication in the network. The task of improving the centrality of a vertex has many applications, as a higher centrality often implies a larger impact on the network or less transportation or administration cost. In this work we study the parameterized complexity of the NP-complete problems Closeness Improvement and Betweenness Improvement in which we ask to improve a given vertex' closeness or betweenness centrality by a given amount through adding a given number of edges to the network. Herein, the closeness of a vertex v sums the multiplicative inverses of distances of other vertices to v and the betweenness sums for each pair of vertices the fraction of shortest paths going through v. Unfortunately, for the natural parameter "number of edges to add" we obtain hardness results, even in rather restricted cases. On the positive side, we also give an island of tractability for the parameter measuring the vertex deletion distance to cluster graphs

The defective seed5 (des5) mutant: effects on barley seed development and HvDek1, HvCr4, and HvSal1 gene regulation

Barley, one of the major small grain crops, is especially important in climatically demanding agricultural areas of the world, with multiple uses within food, feed, and beverage. The barley endosperm is further of special scientific interest due to its three aleurone cell layers, with the potential of bringing forward the molecular understanding of seed development and cell specification from Arabidopsis and maize. Work done in Arabidopsis and maize indicate the presence of conserved seed developmental pathways where Crinkly4 (Cr4), Defective kernel1 (Dek1), and Supernumerary aleurone layer1 (Sal1) are key players. With the use of microscopy, a comprehensive phenotypic characterization of the barley defective seed5 (des5) mutant is presented here. The analysis further extends to molecular quantification of gene expression changes in the des5 mutant by qRT-PCR. Moreover, full-length genomic sequences of the barley orthologues were generated and these were annotated as HvDek1, HvCr4, and HvSal1. The most striking results in this study are the patchy reduction in number of aleurone cells, rudimentary anticlinal aleurone cell walls, and the specific change of HvCr4 expression compared to HvDek1 and HvSal1. The data presented support the involvement of Hvdes5 in establishing aleurone cells. Finally, how these results might affect the current model of aleurone and epidermal cell identity and development is discussed with a speculation regarding a possible role of Des5 in regulating cell division/ secondary cell wall building