Visualization Of Complex Networks Research Articles

Privacy concerns in social media UGC communities: Understanding user behavior sentiments in complex networks

AbstractIn a digital ecosystem where large amounts of data related to user actions are generated every day, important concerns have emerged about the collection, management, and analysis of these data and, according, about user privacy. In recent years, users have been accustomed to organizing in and relying on digital communities to support and achieve their goals. In this context, the present study aims to identify the main privacy concerns in user communities on social media, and how these affect users’ online behavior. In order to better understand online communities in social networks, privacy concerns, and their connection to user behavior, we developed an innovative and original methodology that combines elements of machine learning as a technical contribution. First, a complex network visualization algorithm known as ForceAtlas2 was used through the open-source software Gephi to visually identify the nodes that form the main communities belonging to the sample of UGC collected from Twitter. Then, a sentiment analysis was applied with Textblob, an algorithm that works with machine learning on which experiments were developed with support vector classifier (SVC), multinomial naïve Bayes (MNB), logistic regression (LR), random forest, and classifier (RFC) under the theoretical frameworks of computer-aided text analysis (CATA) and natural language processing (NLP). As a result, a total of 11 user communities were identified: the positive protection software and cybersecurity and eCommerce, the negative privacy settings, personal information and social engineering, and the neutral privacy concerns, hacking, false information, impersonation and cookies data. The paper concludes with a discussion of the results and their relation to user behavior in digital environments and an outline valuable and practical insights into some techniques and challenges related to users’ personal data.

CompositeView: A Network-Based Visualization Tool.

Large networks are quintessential to bioinformatics, knowledge graphs, social network analysis, and graph-based learning. CompositeView is a Python-based open-source application that improves interactive complex network visualization and extraction of actionable insight. CompositeView utilizes specifically formatted input data to calculate composite scores and display them using the Cytoscape component of Dash. Composite scores are defined representations of smaller sets of conceptually similar data that, when combined, generate a single score to reduce information overload. Visualized interactive results are user-refined via filtering elements such as node value and edge weight sliders and graph manipulation options (e.g., node color and layout spread). The primary difference between CompositeView and other network visualization tools is its ability to auto-calculate and auto-update composite scores as the user interactively filters or aggregates data. CompositeView was developed to visualize network relevance rankings, but it performs well with non-network data. Three disparate CompositeView use cases are shown: relevance rankings from SemNet 2.0, an open-source knowledge graph relationship ranking software for biomedical literature-based discovery; Human Development Index (HDI) data; and the Framingham cardiovascular study. CompositeView was stress tested to construct reference benchmarks that define breadth and size of data effectively visualized. Finally, CompositeView is compared to Excel, Tableau, Cytoscape, neo4j, NodeXL, and Gephi.

BC tree-based spectral sampling for big complex network visualization

Graph sampling methods have been used to reduce the size and complexity of big complex networks for graph mining and visualization. However, existing graph sampling methods often fail to preserve the connectivity and important structures of the original graph. This paper introduces a new divide and conquer approach to spectral graph sampling based on graph connectivity, called the BC Tree (i.e., decomposition of a connected graph into biconnected components) and spectral sparsification. Specifically, we present two methods, spectral vertex sampling BC_SV and spectral edge sampling BC_SS by computing effective resistance values of vertices and edges for each connected component. Furthermore, we present DBC_SS and DBC_GD, graph connectivity-based distributed algorithms for spectral sparsification and graph drawing respectively, aiming to further improve the runtime efficiency of spectral sparsification and graph drawing by integrating connectivity-based graph decomposition and distributed computing. Experimental results demonstrate that BC_SV and BC_SS are significantly faster than previous spectral graph sampling methods while preserving the same sampling quality. DBC_SS and DBC_GD obtain further significant runtime improvement over sequential approaches, and DBC_GD further achieves significant improvements in quality metrics over sequential graph drawing layouts.

Quickly evaluating the synergistic effects of top anti-cancer drugs by the computer high performance computing power and complex network visualization

Quickly evaluating the synergistic effects of top anti-cancer drugs by the computer high performance computing power and complex network visualization

A Drug Repurposing Method Based on Drug-Drug Interaction Networks and Using Energy Model Layouts.

Complex network representations of reported drug-drug interactions foster computational strategies that can infer pharmacological functions which, in turn, create incentives for drug repositioning. Here, we use Gephi (a platform for complex network visualization and analysis) to represent a drug-drug interaction network with drug interaction information from DrugBank 4.1. Both modularity class- and force-directed layout ForceAtlas2 are employed to generate drug clusters which correspond to nine specific drug properties. Most drugs comply with their cluster's dominant property; however, some of them seem not to be in a proper position (i.e., in accordance with their already known functions). Such cases, along with cases of drugs that are topologically placed in the overlapping or bordering zones between clusters, may indicate previously unaccounted pharmacologic functions, thus leading to potential repositionings. Out of the 1141 drugs with relevant information on their interactions in DrugBank 4.1, we confirm the predicted properties for 85% of the drugs. The high prediction rate of our methodology suggests that, at least for some of the 15% drugs that seem to be inconsistent with the predicted property, we can get very good repositioning hints. As such, we present illustrative examples of recovered well-known repositionings, as well as recently confirmed pharmacological properties.

Using Complex Network Visualization and Analysis for Uncovering the Inner Dynamics of PSO Algorithm

In this study, we construct a complex network from the inner dynamic of Particle Swarm Optimization algorithm. The subsequent analysis of the network promises to provide useful information for better understanding the dynamic of the swarm that is not acquirable by other means. We present several network visualizations and numerical analysis. We discuss the observations and propose further directions for the research.

Distributed computing of distance‐based graph invariants for analysis and visualization of complex networks

SummaryWe present a new framework for analysis and visualization of complex networks based on structural information retrieved from their distance k‐graphs and B‐matrices. The construction of B‐matrices for graphs with more than 1 million edges requires massive Breadth‐First Search (BFS) computations and is facilitated using new software prepared for distributed environments. Our framework benefits from data parallelism inherent to all‐pair shortest‐path problem and extends Cassovary, an open‐source in‐memory graph processing engine, to enable multinode computation of distance k‐graphs and related graph descriptors. We also introduce a new type of B‐matrix, constructed using clustering coefficient vertex invariant, which can be generated with a computational effort comparable with the one required for a previously known degree B‐matrix, while delivering an additional set of information about graph structure. Our approach enables efficient generation of expressive, multidimensional descriptors useful in graph embedding and graph mining tasks. The experiments showed that the new framework is scalable and for specific all‐pair shortest‐path task provides better performance than existing generic graph processing frameworks. We further present how the developed tools helped in the analysis and visualization of real‐world graphs from Stanford Large Network Dataset Collection. Copyright © 2016 John Wiley & Sons, Ltd.

Ivga: A fast force-directed method for interactive visualization of complex networks

Complex networks play a very important role in various fields of science as data structures, which aggregate information about mutual relationships between numerous objects. The structural properties of these large graphs can be scrutinized throughout their interactive visualization. However, visual analysis of complex networks consisting of |V|∼106+ vertices represents a great challenge for nowadays computer systems both from computational and storage perspective. Therefore, the existing graph drawing methods involving greater than O(|V|) time and space complexity cannot be regarded as promising tools in the advent of the Big Data era. We present here a new and very fast graph drawing method with O(|V|) time and space complexity – ivga (interactive visualization of graphs). We evaluate its usefulness and performance by testing ivga on the large complex networks from the Stanford Large Network Dataset Collection. We demonstrate that ivga allows for very fast interactive visualization of large graphs consisting of up to a few million vertices on a regular laptop what makes it very competitive to other state-of-art graph drawing methods. Particularly, we recommend ivga method for interactive visualization of large non-planar complex networks such as small-world and scale-free social networks. The main concept of ivga can be seriously considered in developing tools for visualization and analysis of really huge networks, with billions of vertices and edges, on Big Data systems.

Adaptive Visualization of Complex Networks with FocalPoint

Maintaining situational awareness of a dynamic global computer network that consists of ten to hundreds of thousands of computers is a complex task for cyber administrators and operators looking to understand, plan and conduct operations in real time. Currently, cyber specialists must manually navigate complex networks by continuous cycles of overviews, drilldowns and manually mapping network incidents to mission impact. This is inefficient as manually maneuvering of network data is laborious, induces cognitive overload, and is prone to errors caused by distractive information resulting in important information and impacts not being seen. We are investigating “FocalPoint” an adaptive level of detail (LOD) recommender system tailored for hierarchical network information structures. FocalPoint reasons about contextual information associated with the network, user task, and user cognitive load to tune the presentation of network visualization displays to improve user performance in perception, comprehension and projection of current situational awareness. Our system is applied to two complex information constructs important to dynamic cyber network operations: network maps and attack graphs. The key innovations include: (a) context-aware automatic tailoring of complex network views, (b) multi-resolution hierarchical graph aggregation, (c) incorporation of new computational models for adaptive-decision making on user tasks, cost/benefit utility and human situation awareness, and (d) user interaction techniques to integrate recommendations into the network viewing system. Our aim is to have a direct impact on planning and operations management for complex networks by; overcoming information overload, preventing tunnel vision, reducing cognitive load, and increasing time available to focus on optimum level of details of the global network space and missions.

Comparison of molecular signatures in large-scale protein interaction networks in normal and cancer conditions of brain, cervix, lung, ovary and prostate

Cancer, the disease of intricateness, has remained beyond our complete perception so far. Network systems biology (termed NSB) is one of the most recent approaches to understand the unsolved problems of cancer development. From this perspective, differential protein networks (PINs) have been developed based on the expression and interaction data of brain, cervix, lung, ovary and prostate for normal and cancer conditions. Differential expression database GeneHub-GEPIS and interaction database STRING were applied for primary data retrieval. Cytoscape platform and related plugins named network analyzer; MCODE and ModuLand were used for visualization of complex networks and subsequent analysis. Significant differences were observed among different common network parameters between normal and cancer states. Moreover, molecular complex numbers and overlapping modularization found to be varying significantly between normal and cancerous tissues. The number of the ranked molecular complex and the nodes involved in the overlapping modules were meaningfully higher in cancer condition. We identified 79 commonly up-regulated and 6 down-regulated proteins in all five tissues. Number of nodes, edges; multi edge node pair, and average number of neighbor are found with significant fluctuations in case of cervix and ovarian tissues. Cluster analysis showed that the association of Myc and Cdk4 proteins is very close with other proteins within the network. Cervix and ovarian tissue showed higher increment of the molecular complex number and overlapping module network during cancer in comparison to normal state. The differential molecular signatures identified from the work can be studied further to understand the cancer signaling process, and potential therapeutic and detection approach.

Temporal Branching Approach for Visual Exploration of Simulation Process in Dynamic Networks

This paper presents a concept for visualization of simulation processes in temporal networks. Core principles are based on interactive real-time visualization of complex networks and dynamic processes. Any modifications in simulation parameters result in division of a timeline into branches. Described concept was integrated into an extended tool for visual analysis and tested on a model of interbank interaction. Proposed developments significantly improve process of visual analysis: pattern detection, search of distinctions and etc.

Complex network analysis of CA3 transcriptome reveals pathogenic and compensatory pathways in refractory temporal lobe epilepsy.

We previously described – studying transcriptional signatures of hippocampal CA3 explants – that febrile (FS) and afebrile (NFS) forms of refractory mesial temporal lobe epilepsy constitute two distinct genomic phenotypes. That network analysis was based on a limited number (hundreds) of differentially expressed genes (DE networks) among a large set of valid transcripts (close to two tens of thousands). Here we developed a methodology for complex network visualization (3D) and analysis that allows the categorization of network nodes according to distinct hierarchical levels of gene-gene connections (node degree) and of interconnection between node neighbors (concentric node degree). Hubs are highly connected nodes, VIPs have low node degree but connect only with hubs, and high-hubs have VIP status and high overall number of connections. Studying the whole set of CA3 valid transcripts we: i) obtained complete transcriptional networks (CO) for FS and NFS phenotypic groups; ii) examined how CO and DE networks are related; iii) characterized genomic and molecular mechanisms underlying FS and NFS phenotypes, identifying potential novel targets for therapeutic interventions. We found that: i) DE hubs and VIPs are evenly distributed inside the CO networks; ii) most DE hubs and VIPs are related to synaptic transmission and neuronal excitability whereas most CO hubs, VIPs and high hubs are related to neuronal differentiation, homeostasis and neuroprotection, indicating compensatory mechanisms. Complex network visualization and analysis is a useful tool for systems biology approaches to multifactorial diseases. Network centrality observed for hubs, VIPs and high hubs of CO networks, is consistent with the network disease model, where a group of nodes whose perturbation leads to a disease phenotype occupies a central position in the network. Conceivably, the chance for exerting therapeutic effects through the modulation of particular genes will be higher if these genes are highly interconnected in transcriptional networks.

Research on Engineering Software Data Formats Conversion Network

Disperse engineering information constitutes a domain of complex relationship, into which researching with complex network could contribute significantly to its organization and preservation. A complex network which takes engineering software data formats as nodes is constructed and its statistical characteristics, such as directed edge or undirected edge, the node number, the edge number, average path length, clustering coefficient and node degree distribution, etc, are analyzed in this paper. A Voronoi diagram based complex network visualization and retrieval method is provided. According to statistical characteristics of constructed network, visualization method calculates network nodes’ position in a two-dimensional plane and divides this plane into Voronoi diagram by feature nodes of network nodes’ clustering and network nodes. Retrieval method restricts compared nodes to nodes in Voronoi domain of feature nodes which are more related to query node, the number of compared nodes is reduced in retrieval process. The experiment result indicates applying this method into data formats conversion path retrieval can ensure retrieval precision and improve retrieval efficiency, thus provide reliable basis for migrating numerous disperse engineering information.