Interaction network

About: Interaction network is a research topic. Over the lifetime, 2700 publications have been published within this topic receiving 113372 citations.

Convolutional Interaction Network for Natural Language Inference

Abstract: Attention-based neural models have achieved great success in natural language inference (NLI) In this paper, we propose the Convolutional Interaction Network (CIN), a general model to capture the interaction between two sentences, which can be an alternative to the attention mechanism for NLI Specifically, CIN encodes one sentence with the filters dynamically generated based on another sentence Since the filters may be designed to have various numbers and sizes, CIN can capture more complicated interaction patterns Experiments on three large datasets demonstrate CIN’s efficacy

SAMNetWeb: identifying condition-specific networks linking signaling and transcription

Abstract: Motivation: High-throughput datasets such as genetic screens, mRNA expression assays and global phospho-proteomic experiments are often difficult to interpret due to inherent noise in each experimental system. Computational tools have improved interpretation of these datasets by enabling the identification of biological processes and pathways that are most likely to explain the measured results. These tools are primarily designed to analyse data from a single experiment (e.g. drug treatment versus control), creating a need for computational algorithms that can handle heterogeneous datasets across multiple experimental conditions at once. Summary: We introduce SAMNetWeb, a web-based tool that enables functional enrichment analysis and visualization of high-throughput datasets. SAMNetWeb can analyse two distinct data types (e.g. mRNA expression and global proteomics) simultaneously across multiple experimental systems to identify pathways activated in these experiments and then visualize the pathways in a single interaction network. Through the use of a multi-commodity flow based algorithm that requires each experiment ‘share’ underlying protein interactions, SAMNetWeb can identify distinct and common pathways across experiments. Availability and implementation: SAMNetWeb is freely available at http://fraenkel.mit.edu/samnetweb. Contact: ude.tim@nimda-leknearf

Topological analysis of a two coupled evolving networks model for business systems

Abstract: In multi-agent systems, the underlying networks are always dynamic and network topologies are always changing over time. Performance analyses of topologies are important for understanding the robustness of the system and also the effects of topology on the system efficiency and effectiveness. In this paper, we present an example of a real-world distributed agent system, a digital business ecosystem (DBE). It is modelled as a two coupled network system. The upper layer is the business network layer where business process between different business agents happen. The lower layer is the underlying P2P communication layer to support communications between the agents. Algorithms for multi-agent tasks negotiation and execution, interaction between agents and the underlying communication network, evolutionary network topology dynamics, are provided. These algorithms consider the two network layers evolving over time, with effects on each other. Through a comprehensive set of discrete event simulation, we investigate the effects of different evolutionary principles inspired by random graph and scale-free network in complex network theory on the topological properties and performance of the underlying network. We also find several rules to design a resilient and efficient P2P network.

Features analysis for identification of date and party hubs in protein interaction network of Saccharomyces Cerevisiae

Abstract: Background It has been understood that biological networks have modular organizations which are the sources of their observed complexity. Analysis of networks and motifs has shown that two types of hubs, party hubs and date hubs, are responsible for this complexity. Party hubs are local coordinators because of their high co-expressions with their partners, whereas date hubs display low co-expressions and are assumed as global connectors. However there is no mutual agreement on these concepts in related literature with different studies reporting their results on different data sets. We investigated whether there is a relation between the biological features of Saccharomyces Cerevisiae's proteins and their roles as non-hubs, intermediately connected, party hubs, and date hubs. We propose a classifier that separates these four classes.

Network Structure of Two-Dimensional Decaying Isotropic Turbulence

Abstract: The present paper reports on our effort to characterize vortical interactions in complex fluid flows through the use of network analysis. In particular, we examine the vortex interactions in two-dimensional decaying isotropic turbulence and find that the vortical interaction network can be characterized by a weighted scale-free network. It is found that the turbulent flow network retains its scale-free behavior until the characteristic value of circulation reaches a critical value. Furthermore, we show that the two-dimensional turbulence network is resilient against random perturbations but can be greatly influenced when forcing is focused towards the vortical structures that are categorized as network hubs. These findings can serve as a network-analytic foundation to examine complex geophysical and thin-film flows and take advantage of the rapidly growing field of network theory, which complements ongoing turbulence research based on vortex dynamics, hydrodynamic stability, and statistics. While additional work is essential to extend the mathematical tools from network analysis to extract deeper physical insights of turbulence, an understanding of turbulence based on the interaction-based network-theoretic framework presents a promising alternative in turbulence modeling and control efforts.