Deep Packet Inspection Using Message Passing Networks

We propose a solution based on message passing bipartite networks, for deep packet inspection, which addresses both speed and memory issues, which are limiting factors in current solutions. We report on a preliminary implementation and propose a parallel architecture.

Contagion in financial networks: a network theory and agent-based approaches to modeling the spread of risk in financial systems

Esta dissertação estuda a propagação de crises sobre o sistema financeiro. Mais especi- ficamente, busca-se desenvolver modelos que permitam simular como um determinado choque econômico atinge determinados agentes do sistema financeiro e apartir dele se propagam, transformando-se em um problema sistêmico. A dissertação é dividida em dois capítulos,além da introdução. O primeiro capítulo desenvolve um modelo de propa- gação de crises em fundos de investimento baseado em ciência das redes.Combinando dois modelos de propagação em redes financeiras, um simulando a propagação de perdas em redes bipartites de ativos e agentes financeiros e o outro simulando a propagação de perdas em uma rede de investimentos diretos em quotas de outros agentes, desenvolve-se um algoritmo para simular a propagação de perdas através de ambos os mecanismos e utiliza-se este algoritmo para simular uma crise no mercado brasileiro de fundos de investimento. No capítulo 2,desenvolve-se um modelo de simulação baseado em agentes, com agentes financeiros, para simular propagação de um choque que afeta o mercado de operações compromissadas.Criamos também um mercado artificial composto por bancos, hedge funds e fundos de curto prazo e simulamos a propagação de um choque de liquidez sobre um ativo de risco securitizando utilizado para colateralizar operações compromissadas dos bancos.

Redes bipartitas: aplicación a un caso económico real

[ES]El TFG consta de dos partes: en la primera se expondrá la teoría sobre las redes que se utilizarán en la segunda parte, que consistirá en el análisis de un caso real donde se aplicarán las redes estudiadas en la primera parte.

Aninhamento em redes bipartidas

We present a nestedness index that measures the nestedness pattern of bipartite networks, a problem that arises in theoretical ecology. Our measure is derived using the sum of distances of the occupied elements in the adjacency matrix of the network. This index quantifies directly the deviation of a given matrix from the nested pattern. In the most simple case the distance of the matrix element ai,j is di,j = i+j, the Manhattan distance. A generic distance is obtained as di,j = (i¬ + j¬)1/¬. The nestedness índex is defined by = 1 − where is the temperature of the matrix. We construct the temperature index using two benchmarks: the distance of the complete nested matrix that corresponds to zero temperature and the distance of the average random matrix that is defined as temperature one. We discuss an important feature of the problem: matrix occupancy. We address this question using a metric index ¬ that adjusts for matrix occupancy

Structure and mechanism of diet specialisation: testing models of individual variation in resource use with sea otters

Studies of consumer-resource interactions suggest that individual diet specialisation is empirically widespread and theoretically important to the organisation and dynamics of populations and communities. We used weighted networks to analyze the resource use by sea otters, testing three alternative models for how individual diet specialisation may arise. As expected, individual specialisation was absent when otter density was low, but increased at high-otter density. A high-density emergence of nested resource-use networks was consistent with the model assuming individuals share preference ranks. However, a density-dependent emergence of a non-nested modular network for core resources was more consistent with the competitive refuge model. Individuals from different diet modules showed predictable variation in rank-order prey preferences and handling times of core resources, further supporting the competitive refuge model. Our findings support a hierarchical organisation of diet specialisation and suggest individual use of core and marginal resources may be driven by different selective pressures.

Design of nonequivalent self-routing networks based on a matrix model

In earlier work, nonisomorphic graphs have been converted into networks to realize Multistage Interconnection networks, which are topologically nonequivalent to the Baseline network. The drawback of this technique is that these nonequivalent networks are not guaranteed to be self-routing, because each node in the graph model can be replaced by a (2 × 2) switch in any one of the four different configurations. Hence, the problem of routing in these networks remains unsolved. Moreover, nonisomorphic graphs were obtained by interconnecting bipartite loops in a heuristic manner; the heuristic nature of this procedure makes it difficult to guarantee full connectivity in large networks. We solve these problems through a direct approach, in which a matrix model for self-routing networks is developed. An example is given to show that this model encompases nonequivalent self-routing networks. This approach has the additional advantage in that the matrix model itself ensures full connectivity.

Bilateral Bargaining in Networks

Each connected pair of nodes in a network can jointly produce one unit of surplus. A maximum number of linked nodes is selected in every period to bargain bilaterally over the division of the surplus, according to the protocol proposed by Rubinstein and Wolinsky (Econometrica 53 (1985), 1133-1150). All pairs, that reach an agreement, obtain the (discounted) payoffs and are removed from the network. This bargaining game has a unique subgame perfect equilibrium that induces the Dulmage-Mendelsohn decomposition (partition) of the bipartite network (of the set of nodes in this network).

The consumer litigation industry: chasing dragon kings in lawyer-client networks

The community of lawyers and their clients form a scale-free bipartite network that develops naturally as the outcome of the recommendation process through which lawyers form their client base. This process is an example of preferential attachment where lawyers with more clients are more likely to be recommended to new clients. Consumer litigation is an important market for lawyers. In large consumer societies, there always a signi cant amount of consumption disputes that escalate to court. In this paper we analyze a dataset of thousands of lawsuits, reconstructing the lawyer-client network embedded in the data. Analyzing the degree distribution of this network we noticed that it follows that of a scale-free network built by preferential attachment, but for a few lawyers with much larger client base than could be expected by preferential attachment. Incidentally, most of these also gured on a list put together by the judiciary of Lawyers which openly advertised the bene ts of consumer litigation. According to the code of ethics of their profession, lawyers should not stimulate clients into litigation, but it is not strictly illegal. From a network formation point of view, this stimulation can be seen as a separate growth mechanism than preferential attachment alone. In this paper we nd that this composite growth can be detected by a simple statistical test, as simulations show that lawyers which use both mechanisms quickly become the \Dragon-Kings" of the distribution of the number of clients per lawyer.

Coded slotted aloha with multipacket reception over block fading channels (for satellite networks)

Random access (RA) protocols are normally used in a satellite networks for initial terminal access and are particularly effective since no coordination is required. On the other hand, contention resolution diversity slotted Aloha (CRDSA), irregular repetition slotted Aloha (IRSA) and coded slotted Aloha (CSA) has shown to be more efficient than classic RA schemes as slotted Aloha, and can be exploited also when short packets transmissions are done over a shared medium. In particular, they relies on burst repetition and on successive interference cancellation (SIC) applied at the receiver. The SIC process can be well described using a bipartite graph representation and exploiting tools used for analyze iterative decoding. The scope of my Master Thesis has been to described the performance of such RA protocols when the Rayleigh fading is taken into account. In this context, each user has the ability to correctly decode a packet also in presence of collision and when SIC is considered this may result in multi-packet reception. Analysis of the SIC procedure under Rayleigh fading has been analytically derived for the asymptotic case (infinite frame length), helping the analysis of both throughput and packet loss rates. An upper bound of the achievable performance has been analytically obtained. It can be show that in particular channel conditions the throughput of the system can be greater than one packets per slot which is the theoretical limit of the Collision Channel case.

Modeling the evolution of item rating networks using time-domain preferential attachment

The understanding of the structure and dynamics of the intricate network of connections among people that consumes products through Internet appears as an extremely useful asset in order to study emergent properties related to social behavior. This knowledge could be useful, for example, to improve the performance of personal recommendation algorithms. In this contribution, we analyzed five-year records of movie-rating transactions provided by Netflix, a movie rental platform where users rate movies from an online catalog. This dataset can be studied as a bipartite user-item network whose structure evolves in time. Even though several topological properties from subsets of this bipartite network have been reported with a model that combines random and preferential attachment mechanisms [Beguerisse Díaz et al., 2010], there are still many aspects worth to be explored, as they are connected to relevant phenomena underlying the evolution of the network. In this work, we test the hypothesis that bursty human behavior is essential in order to describe how a bipartite user-item network evolves in time. To that end, we propose a novel model that combines, for user nodes, a network growth prescription based on a preferential attachment mechanism acting not only in the topological domain (i.e. based on node degrees) but also in time domain. In the case of items, the model mixes degree preferential attachment and random selection. With these ingredients, the model is not only able to reproduce the asymptotic degree distribution, but also shows an excellent agreement with the Netflix data in several time-dependent topological properties.

Information content: assessing meso-scale structures in complex networks

We propose a novel measure to assess the presence of meso-scale structures in complex networks. This measure is based on the identi?cation of regular patterns in the adjacency matrix of the network, and on the calculation of the quantity of information lost when pairs of nodes are iteratively merged. We show how this measure is able to quantify several meso-scale structures, like the presence of modularity, bipartite and core-periphery con?gurations, or motifs. Results corresponding to a large set of real networks are used to validate its ability to detect non-trivial topological patterns.