Network architecture for large-scale distributed virtual environments

Distributed Virtual Environments (DVEs) provide 3D graphical computer generated environments with stereo sound, supporting real-time collaboration between potentially large numbers of users distributed around the world. Early DVEs has been used over local area networks (LANs). Recently with the Internet's development into the most common embedding for DVEs these distributed applications have been moved towards an exploiting IP networks. This has brought the scalability challenges into the DVEs evolution. The network bandwidth resource is the more limited resource of the DVE system and to improve the DVE's scalability it is necessary to manage carefully this resource. To achieve the saving in the network bandwidth the different types of the network traffic that is produced by the DVEs have to be considered. DVE applications demand· exchange of the data that forms different types of traffic such as a computer data type, video and audio, and a 3D data type to keep the consistency of the application's state. The problem is that the meeting of the QoS requirements of both control and continuous media traffic already have been covered by the existing research. But QoS for transfer of the 3D information has not really been considered. The 3D DVE geometry traffic is very bursty in nature and places a high demands on the network for short intervals of time due to the quite large size of the 3D models and the DVE application requirements to transmit a 3D data as quick as possible. The main motivation in carrying out the work presented in this thesis is to find a solution to improve the scalability of the DVE applications by a consideration the QoS requirements of the 3D DVE geometrical data type. In this work we are investigating the possibility to decrease the network bandwidth utilization by the 3D DVE traffic using the level of detail (LOD) concept and the active networking approach. The background work of the thesis surveys the DVE applications and the scalability requirements of the DVE systems. It also discusses the active networks and multiresolution representation and progressive transmission of the 3D data. The new active networking approach to the transmission of the 3D geometry data within the DVE systems is proposed in this thesis. This approach enhances the currently applied peer-to-peer DVE architecture by adding to the peer-to-peer multicast neny_ork layer filtering of the 3D flows an application level filtering on the active intermediate nodes. The active router keeps the application level information about the placements of users. This information is used by active routers to prune more detailed 3D data flows (higher LODs) in the multicast tree arches that are linked to the distance DVE participants. The exploration of possible benefits of exploiting the proposed active approach through the comparison with the non-active approach is carried out using the simulation­based performance modelling approach. Complex interactions between participants in DVE application and a large number of analyzed variables indicate that flexible simulation is more appropriate than mathematical modelling. To build a test bed will not be feasible. Results from the evaluation demonstrate that the proposed active approach shows potential benefits to the improvement of the DVE's scalability but the degree of improvement depends on the users' movement pattern. Therefore, other active networking methods to support the 3D DVE geometry transmission may also be required

Detecting communities of triangles in complex networks using spectral optimization

The study of the sub-structure of complex networks is of major importance to relate topology and functionality. Many efforts have been devoted to the analysis of the modular structure of networks using the quality function known as modularity. However, generally speaking, the relation between topological modules and functional groups is still unknown, and depends on the semantic of the links. Sometimes, we know in advance that many connections are transitive and, as a consequence, triangles have a specific meaning. Here we propose the study of the modular structure of networks considering triangles as the building blocks of modules. The method generalizes the standard modularity and uses spectral optimization to find its maximum. We compare the partitions obtained with those resulting from the optimization of the standard modularity in several real networks. The results show that the information reported by the analysis of modules of triangles complements the information of the classical modularity analysis.Comment: Computer Communications (in press

Hierarchical clustering of bipartite data sets based on the statistical significance of coincidences

When some 'entities' are related by the 'features' they share they are amenable to a bipartite network representation. Plant-pollinator ecological communities, co-authorship of scientific papers, customers and purchases, or answers in a poll, are but a few examples. Analyzing clustering of such entities in the network is a useful tool with applications in many fields, like internet technology, recommender systems, or detection of diseases. The algorithms most widely applied to find clusters in bipartite networks are variants of modularity optimization. Here, we provide a hierarchical clustering algorithm based on a dissimilarity between entities that quantifies the probability that the features shared by two entities are due to mere chance. The algorithm performance is O(n2) when applied to a set of n entities, and its outcome is a dendrogram exhibiting the connections of those entities. Through the introduction of a 'susceptibility' measure we can provide an 'optimal' choice for the clustering as well as quantify its quality. The dendrogram reveals further useful structural information though -like the existence of subclusters within clusters or of nodes that do not fit in any cluster. We illustrate the algorithm by applying it first to a set of synthetic networks, and then to a selection of examples. We also illustrate how to transform our algorithm into a valid alternative for one-mode networks as well, and show that it performs at least as well as the standard, modularity-based algorithms- with a higher numerical performance. We provide an implementation of the algorithm in python freely accessible from GitHub.This research has been funded by the Spanish Ministerio de Ciencia e, Innovación FEDER funds of the European Union support, under project PGC2018-098186-B-I00

Multiresolution community detection in multilayer networks

The aim of this thesis is to apply multiresolution to Infomap, a community detection algorithm based in the map equation that work in monolayer and multilayer networks

An agent-driven semantical identifier using radial basis neural networks and reinforcement learning

Due to the huge availability of documents in digital form, and the deception possibility raise bound to the essence of digital documents and the way they are spread, the authorship attribution problem has constantly increased its relevance. Nowadays, authorship attribution,for both information retrieval and analysis, has gained great importance in the context of security, trust and copyright preservation. This work proposes an innovative multi-agent driven machine learning technique that has been developed for authorship attribution. By means of a preprocessing for word-grouping and time-period related analysis of the common lexicon, we determine a bias reference level for the recurrence frequency of the words within analysed texts, and then train a Radial Basis Neural Networks (RBPNN)-based classifier to identify the correct author. The main advantage of the proposed approach lies in the generality of the semantic analysis, which can be applied to different contexts and lexical domains, without requiring any modification. Moreover, the proposed system is able to incorporate an external input, meant to tune the classifier, and then self-adjust by means of continuous learning reinforcement.Comment: Published on: Proceedings of the XV Workshop "Dagli Oggetti agli Agenti" (WOA 2014), Catania, Italy, Sepember. 25-26, 201

Uncovering the mesoscale structure of the credit default swap market to improve portfolio risk modelling

One of the most challenging aspects in the analysis and modelling of financial markets, including Credit Default Swap (CDS) markets, is the presence of an emergent, intermediate level of structure standing in between the microscopic dynamics of individual financial entities and the macroscopic dynamics of the market as a whole. This elusive, mesoscopic level of organisation is often sought for via factor models that ultimately decompose the market according to geographic regions and economic industries. However, at a more general level the presence of mesoscopic structure might be revealed in an entirely data-driven approach, looking for a modular and possibly hierarchical organisation of the empirical correlation matrix between financial time series. The crucial ingredient in such an approach is the definition of an appropriate null model for the correlation matrix. Recent research showed that community detection techniques developed for networks become intrinsically biased when applied to correlation matrices. For this reason, a method based on Random Matrix Theory has been developed, which identifies the optimal hierarchical decomposition of the system into internally correlated and mutually anti-correlated communities. Building upon this technique, here we resolve the mesoscopic structure of the CDS market and identify groups of issuers that cannot be traced back to standard industry/region taxonomies, thereby being inaccessible to standard factor models. We use this decomposition to introduce a novel default risk model that is shown to outperform more traditional alternatives.Comment: Quantitative Finance (2021

Unsupervised Structural Embedding Methods for Efficient Collective Network Mining

How can we align accounts of the same user across social networks? Can we identify the professional role of an email user from their patterns of communication? Can we predict the medical effects of chemical compounds from their atomic network structure? Many problems in graph data mining, including all of the above, are defined on multiple networks. The central element to all of these problems is cross-network comparison, whether at the level of individual nodes or entities in the network or at the level of entire networks themselves. To perform this comparison meaningfully, we must describe the entities in each network expressively in terms of patterns that generalize across the networks. Moreover, because the networks in question are often very large, our techniques must be computationally efficient. In this thesis, we propose scalable unsupervised methods that embed nodes in vector space by mapping nodes with similar structural roles in their respective networks, even if they come from different networks, to similar parts of the embedding space. We perform network alignment by matching nodes across two or more networks based on the similarity of their embeddings, and refine this process by reinforcing the consistency of each node’s alignment with those of its neighbors. By characterizing the distribution of node embeddings in a graph, we develop graph-level feature vectors that are highly effective for graph classification. With principled sparsification and randomized approximation techniques, we make all our methods computationally efficient and able to scale to graphs with millions of nodes or edges. We demonstrate the effectiveness of structural node embeddings on industry-scale applications, and propose an extensive set of embedding evaluation techniques that lay the groundwork for further methodological development and application.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162895/1/mheimann_1.pd

Overview of contextual tracking approaches in information fusion

Proceedings of: Geospatial InfoFusion III. 2-3 May 2013 Baltimore, Maryland, United States.Many information fusion solutions work well in the intended scenarios; but the applications, supporting data, and capabilities change over varying contexts. One example is weather data for electro-optical target trackers of which standards have evolved over decades. The operating conditions of: technology changes, sensor/target variations, and the contextual environment can inhibit performance if not included in the initial systems design. In this paper, we seek to define and categorize different types of contextual information. We describe five contextual information categories that support target tracking: (1) domain knowledge from a user to aid the information fusion process through selection, cueing, and analysis, (2) environment-to-hardware processing for sensor management, (3) known distribution of entities for situation/threat assessment, (4) historical traffic behavior for situation awareness patterns of life (POL), and (5) road information for target tracking and identification. Appropriate characterization and representation of contextual information is needed for future high-level information fusion systems design to take advantage of the large data content available for a priori knowledge target tracking algorithm construction, implementation, and application.Publicad

A Survey on Visual Analytics of Social Media Data

The unprecedented availability of social media data offers substantial opportunities for data owners, system operators, solution providers, and end users to explore and understand social dynamics. However, the exponential growth in the volume, velocity, and variability of social media data prevents people from fully utilizing such data. Visual analytics, which is an emerging research direction, ha..