Neighborhood-based bridge node centrality tuple for complex network analysis

We define a bridge node to be a node whose neighbor nodes are sparsely connected to each other and are likely to be part of different components if the node is removed from the network. We propose a computationally light neighborhood-based bridge node centrality (NBNC) tuple that could be used to identify the bridge nodes of a network as well as rank the nodes in a network on the basis of their topological position to function as bridge nodes. The NBNC tuple for a node is asynchronously computed on the basis of the neighborhood graph of the node that comprises of the neighbors of the node as vertices and the links connecting the neighbors as edges. The NBNC tuple for a node has three entries: the number of components in the neighborhood graph of the node, the algebraic connectivity ratio of the neighborhood graph of the node and the number of neighbors of the node. We analyze a suite of 60 complex real-world networks and evaluate the computational lightness, effectiveness, efficiency/accuracy and uniqueness of the NBNC tuple vis-a-vis the existing bridgeness related centrality metrics and the Louvain community detection algorithm.

Dam type and topological position govern ice-marginal lake area change in Alaska and NW Canada between 1984 and 2019

Ice-marginal lakes impact glacier mass balance, water resources, and ecosystem dynamics, and can produce catastrophic glacial lake outburst floods (GLOFs) via sudden drainage. Multitemporal inventories of ice-marginal lakes are a critical first step in understanding the drivers of historic change, predicting future lake evolution, and assessing GLOF hazards. Here, we use Landsat-era satellite imagery and supervised classification to semi-automatically delineate lake outlines for four ~5 year time periods between 1984 and 2019 in Alaska and northwest Canada. Overall, ice-marginal lakes in the region have grown in total number (+176 lakes, 36 % increase) and area (+467 km2, 57 % increase) between the time periods of 1984–1988 and 2016–2019. However, changes in lake numbers and area were notably unsteady and nonuniform. We demonstrate that lake area changes are connected to dam type (moraine, bedrock, ice, or supraglacial) and topological position (proglacial, detached, unconnected, ice, or supraglacial), with important differences in lake behavior between the sub-groups. In strong contrast to all other dam types, ice-dammed lakes decreased in number (−9, 13 % decrease) and area (−56 km2, 43 % decrease), while moraine-dammed lakes increased (+59, 28 % and +468 km2, 85 % for number and area, respectively), a faster rate than the average when considering all dam types together. Proglacial lakes experienced the largest area changes and rate of change out of any topological position throughout the period of study. By tracking individual lakes through time and categorizing lakes by dam type, subregion, and topological position, we are able to parse trends that would otherwise be aliased if these characteristics were not considered. This work highlights the importance of such lake characterization when performing ice-marginal lake inventories, and provides insight into the physical processes driving recent ice-marginal lake evolution.

A hybrid node ranking technique for finding influential nodes in complex social networks

Purpose In today’s digital era, data pertaining to scientific research have attracted considerable attention of researchers. Data of scientific publications can be modeled in the form of networks such as citation networks, co-citation networks, collaboration networks, and others. Identification and ranking of important nodes in such networks is useful in many applications, such as finding most influential papers, most productive researchers, pattern of citation, and many more. The paper aims to discuss this issue. Design/methodology/approach A number of methods are available in literature for node ranking, and K-shell decomposition is one such method. This method categorizes nodes in different groups based on their topological position. The shell number of a node provides useful insights about the node’s importance in the network. It has been found that shells produced by the K-shell method need to be further refined to quantify the influence of the nodes aptly. In this work, a method has been developed, which ranks nodes by taking the core(s) as the origin and second-order neighborhood of a node as its immediate sphere of influence. Findings It is found that the performance of the proposed technique is either comparable or better than other methods in terms of correctness and accuracy. In case of assigning different ranks to nodes, the performance of the proposed technique is far more superior to existing methods. The proposed method can be used to rank authors, research articles, and fields of research. Originality/value The proposed method ranks nodes by their global position in a network as well as their local sphere of information. It leads to better quantification of a node’s impact. This method is found to be better in terms of accuracy and correctness. In case of assigning different ranks to nodes, the performance of the proposed technique is far more superior to existing methods.

Aggregating a Plankton Food Web: Mathematical versus Biological Approaches

Species are embedded in a web of intricate trophic interactions. Understanding the functional role of species in food webs is of fundamental interests. This is related to food web position, so positional similarity may provide information about functional overlap. Defining and quantifying similar trophic functioning can be addressed in different ways. We consider two approaches. One is of mathematical nature involving network analysis where unique species can be defined as those whose topological position is very different to others in the same food web. A species is unique if it has very different connection pattern compared to others. The second approach is of biological nature, based on trait-based aggregations. Unique species are not easy to aggregate with others because their traits are not in common with the ones of most others. Our goal here is to illustrate how mathematics can provide an alternative perspective on species aggregation, and how this is related to its biological counterpart. We illustrate these approaches using a toy food web and a real food web and demonstrate the sensitive relationships between those approaches. The trait-based aggregation focusing on the trait values of size (sv) can be best predicted by the mathematical aggregation algorithms.

Social Media’s Position in Web Within the Scope of Online Information Flow

World Wide Web (Web) is commercialized at the very end of 20th century and now, in the 21st century, almost half of the human society is using it. Web technologies have evolved and a relatively small set of them has a capacity of simulating complexity of sociality via interpersonal interactions; to define this small set many terms have been suggested, yet social media has been widely used by many scholars. Social media is a set of online communication networks that constitutes a crucial hub for producing, accessing and diffusing information. Inquiry on understanding the online flow of the information, it is essential to understand the topological position of social media within web’s mesh structure. In this paper at first communication and information terms will be defined and social media will be discussed within the scope of complexity and information flow terms from network science and communication science perspective. At last, a graph analysis example on social media’s topological position within web will be shared to emphasize social media’s importance on online information flow.