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In social networks, individuals’ decisions are strongly influenced by recommendations from their friends, acquaintances, and favorite renowned personalities. The popularity of online social networking platforms makes them the prime venues to advertise products and promote opinions. The Influence Maximization (IM) problem entails selecting a seed set of users that maximizes the influence spread, i.e., the expected number of users positively influenced by a stochastic diffusion process triggered by the seeds. Engineering and analyzing IM algorithms remains a difficult and demanding task due to the NP-hardness of the problem and the stochastic nature of the diffusion processes. Despite several heuristics being introduced, they often fail in providing enough information on how the network topology affects the diffusion process, precious insights that could help researchers improve their seed set selection. In this paper, we present VAIM, a visual analytics system that supports users in analyzing, evaluating, and comparing information diffusion processes determined by different IM algorithms. Furthermore, VAIM provides useful insights that the analyst can use to modify the seed set of an IM algorithm, so to improve its influence spread. We assess our system by: $(i)$ (i) a qualitative evaluation based on a guided experiment with two domain experts on two different data sets; $(ii)$ (i i) a quantitative estimation of the value of the proposed visualization through the ICE-T methodology by Wall et al. (IEEE TVCG - 2018). The twofold assessment indicates that VAIM effectively supports our target users in the visual analysis of the performance of IM algorithms. [ABSTRACT FROM AUTHOR]

The use of graph visualization approaches to present and analyze complex data is taking a leading role in conveying information and knowledge to users in many application domains. This creates the need of developing efficient and effective algorithms that automatically compute graph layouts. In this respect, force-directed algorithms are arguably among the most popular graph layout techniques. Aimed at leveraging the potential of modern distributed graph algorithms platforms, we present Multi-GiLA, the first multilevel force-directed graph visualization algorithm based on a vertex-centric computation paradigm. We implemented Multi-GiLA using the Apache Giraph platform. Experiments show that it can be successfully applied to compute high quality layouts of very large graphs on inexpensive cloud computing platforms. [ABSTRACT FROM AUTHOR]

One of the most challenging issues in mining information from the World Wide Web is the design of systems that present the data to the end user by clustering them into meaningful semantic categories. We show that the analysis of the results of a clustering engine can significantly take advantage of enhanced graph drawing and visualization techniques. We propose a graph-based user interface for Web clustering engines that makes it possible for the user to explore and visualize the different semantic categories and their relationships at the desired level of detail. [ABSTRACT FROM AUTHOR]

A diagram is a drawing on the plane that represents a graph-like structure, where nodes are represented by symbols and edges are represented by curves connecting pairs of symbols. An automatic layout facility is a tool that receives as input a graph-like structure and is able to produce a diagram that nicely represents such a structure. Many systems use diagrams in the interaction with the users; thus, automatic layout facilities and algorithms for graphs layout have been extensively studied in the last years. We present a new approach in designing an automatic layout facility. Our approach is based on a modular management of a large collection of algorithms and on a strategy that, given the requirements of an application, selects a suitable algorithm for such requirements. The proposed approach has been used for designing the automatic layout facility of Diagram Server, a network server that offers to its clients several facilities for managing diagrams. [ABSTRACT FROM AUTHOR]

Many different approaches have been proposed for the challenging problem of visually analyzing large networks. Clustering is one of the most promising. In this paper, we propose a new clustering technique whose goal is that of producing both intracluster graphs and intercluster graph with desired topological properties. We formalize this concept in the (X,Y) -clustering framework, where Y is the class that defines the desired topological properties of intracluster graphs and X is the class that defines the desired topological properties of the intercluster graph. By exploiting this approach, hybrid visualization tools can effectively combine different node-link and matrix-based representations, allowing users to interactively explore the graph by xpansion/contraction of clusters without loosing their mental map. As a proof of concept, we describe the system Visual Hybrid (X,Y)-clustering (VHYXY) that implements our approach and we present the results of case studies to the visual analysis of social networks. [ABSTRACT FROM PUBLISHER]