372 research outputs found
PageRank algorithm for Directed Hypergraph
During the last two decades, we easilly see that the World Wide Web's link
structure is modeled as the directed graph. In this paper, we will model the
World Wide Web's link structure as the directed hypergraph. Moreover, we will
develop the PageRank algorithm for this directed hypergraph. Due to the lack of
the World Wide Web directed hypergraph datasets, we will apply the PageRank
algorithm to the metabolic network which is the directed hypergraph itself. The
experiments show that our novel PageRank algorithm is successfully applied to
this metabolic network.Comment: 6 page
ChoiceRank: Identifying Preferences from Node Traffic in Networks
Understanding how users navigate in a network is of high interest in many
applications. We consider a setting where only aggregate node-level traffic is
observed and tackle the task of learning edge transition probabilities. We cast
it as a preference learning problem, and we study a model where choices follow
Luce's axiom. In this case, the marginal counts of node visits are a
sufficient statistic for the transition probabilities. We show how to
make the inference problem well-posed regardless of the network's structure,
and we present ChoiceRank, an iterative algorithm that scales to networks that
contains billions of nodes and edges. We apply the model to two clickstream
datasets and show that it successfully recovers the transition probabilities
using only the network structure and marginal (node-level) traffic data.
Finally, we also consider an application to mobility networks and apply the
model to one year of rides on New York City's bicycle-sharing system.Comment: Accepted at ICML 201
A Mobile Ambients-based Approach for Network Attack Modelling and Simulation
Attack Graphs are an important support for assessment and subsequent improvement of network security. They reveal possible paths an attacker can take to break through security perimeters and traverse a network to reach valuable assets deep inside the network. Although scalability is no longer the main issue, Attack Graphs still have some problems that make them less useful in practice. First, Attack Graphs remain difficult to relate to the network topology. Second, Attack Graphs traditionally only consider the exploitation of vulnerable hosts. Third, Attack Graphs do not rely on automatic identification of potential attack targets. We address these gaps in our MsAMS (Multi-step Attack Modelling and Simulation) tool, based on Mobile Ambients. The tool not only allows the modelling of more static aspects of the network, such as the network topology, but also the dynamics of network attacks. In addition to Mobile Ambients, we use the PageRank algorithm to determine targets and hub scores produced by the HITS (Hypertext Induced Topic Search) algorithm to guide the simulation of an attacker searching for targets
A Mobile Ambients-based Approach for Network Attack Modelling and Simulation
Attack Graphs are an important support for assessment and subsequent improvement of network security. They reveal possible paths an attacker can take to break through security perimeters and traverse a network to reach valuable assets deep inside the network. Although scalability is no longer the main issue, Attack Graphs still have some problems that make them less useful in practice. First, Attack Graphs remain difficult to relate to the network topology. Second, Attack Graphs traditionally only consider the exploitation of vulnerable hosts. Third, Attack Graphs do not rely on automatic identification of potential attack targets. We address these gaps in our MsAMS (Multi-step Attack Modelling and Simulation) tool, based on Mobile Ambients. The tool not only allows the modelling of more static aspects of the network, such as the network topology, but also the dynamics of network attacks. In addition to Mobile Ambients, we use the PageRank algorithm to determine targets and hub scores produced by the HITS (Hypertext Induced Topic Search) algorithm to guide the simulation of an attacker searching for targets
Network Capacity Bound for Personalized PageRank in Multimodal Networks
In a former paper the concept of Bipartite PageRank was introduced and a
theorem on the limit of authority flowing between nodes for personalized
PageRank has been generalized. In this paper we want to extend those results to
multimodal networks. In particular we introduce a hypergraph type that may be
used for describing multimodal network where a hyperlink connects nodes from
each of the modalities. We introduce a generalisation of PageRank for such
graphs and define the respective random walk model that can be used for
computations. we finally state and prove theorems on the limit of outflow of
authority for cases where individual modalities have identical and distinct
damping factors.Comment: 28 pages. arXiv admin note: text overlap with arXiv:1702.0373
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