Graph ambiguity

In this paper, we propose a rigorous way to define the concept of ambiguity in the domain of graphs. In past studies, the classical definition of ambiguity has been derived starting from fuzzy set and fuzzy information theories. Our aim is to show that also in the domain of the graphs it is possible to derive a formulation able to capture the same semantic and mathematical concept. To strengthen the theoretical results, we discuss the application of the graph ambiguity concept to the graph classification setting, conceiving a new kind of inexact graph matching procedure. The results prove that the graph ambiguity concept is a characterizing and discriminative property of graphs. (C) 2013 Elsevier B.V. All rights reserved

Persistence Bag-of-Words for Topological Data Analysis

Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs). PDs exhibit, however, complex structure and are difficult to integrate in today's machine learning workflows. This paper introduces persistence bag-of-words: a novel and stable vectorized representation of PDs that enables the seamless integration with machine learning. Comprehensive experiments show that the new representation achieves state-of-the-art performance and beyond in much less time than alternative approaches.Comment: Accepted for the Twenty-Eight International Joint Conference on Artificial Intelligence (IJCAI-19). arXiv admin note: substantial text overlap with arXiv:1802.0485

Distributed Multi-Objective Evolutionary Algorithm For Dynamic Multi-Characteristic Social Networks Clustering

In this information era, social media and online social networks have become a huge data source. The social network perspective provides a clear way of analyzing the structure of whole social entities. These social media and online social networks are a virtual representation of real life as they represent real life relations between social actors (people). The primary focus of this study is to propose an algorithm and its implementation for clustering of multi-characteristic dynamic graphs in general, and multi-characteristic dynamic online social networks in specific. Social networks are typically stored as graph data (edges lists mostly), and dynamically changes with time either by expanding or shrinking. The topology of the graph data also changes along with the values for the relationships between nodes. Several algorithms were proposed for clustering, but only few of them deals with multi-characteristic and dynamic networks. Most of the proposed algorithms work for static networks or small networks and a very small number of algorithms work for huge and dynamic networks. In this study a practical algorithm is proposed which uses a combination of multi-objective evolutionary algorithms, distributed file systems and nested hybrid-indexing techniques to cluster the multi-characteristic dynamic huge social networks. The results of this work show a fast clustering system that is adaptive to dynamic interactions in social networks also provides a reliable distributed framework for BIG data analysi

Scan path visualization and comparison using visual aggregation techniques

We demonstrate the use of different visual aggregation techniques to obtain non-cluttered visual representations of scanpaths. First, fixation points are clustered using the mean-shift algorithm. Second, saccades are aggregated using the Attribute-Driven Edge Bundling (ADEB) algorithm that handles a saccades direction, onset timestamp, magnitude or their combination for the edge compatibility criterion. Flow direction maps, computed during bundling, can be visualized separately (vertical or horizontal components) or as a single image using the Oriented Line Integral Convolution (OLIC) algorithm. Furthermore, cosine similarity between two flow direction maps provides a similarity map to compare two scanpaths. Last, we provide examples of basic patterns, visual search task, and art perception. Used together, these techniques provide valuable insights about scanpath exploration and informative illustrations of the eye movement data