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Interaction network

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


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Journal ArticleDOI
Chao Yang1, Lianhai Miao1, Bin Jiang1, Dongsheng Li2, Da Cao1 
TL;DR: A neural network-based solution for user generated list recommendation, which can leverage both item-level information and list- level information to improve performance and can outperform state-of-the-art methods in both item recommendation and list recommendation in terms of accuracy.
Abstract: Recommending user generated lists (e.g., playlists) has become an emerging task in many online systems. Many existing list recommendation methods predict user preferences on lists by aggregating their preferences on individual items, which neglects the list-level information (e.g., list attributes) and thus results in suboptimal performance. This paper proposes a neural network-based solution for user generated list recommendation, which can leverage both item-level information and list-level information to improve performance. Firstly, a representation learning network with attention and gate mechanism is proposed to learn the user embeddings, item embeddings and list embeddings simultaneously. Then, an interaction network is proposed to learn user–item interactions and user–list interactions, in which the two kinds of interactions can share the convolution layers to further improve performance. Experimental studies on two real-world datasets demonstrate that (1) the proposed representation learning network can learn more representative user/item/list embedding than existing methods and (2) the proposed solution can outperform state-of-the-art methods in both item recommendation and list recommendation in terms of accuracy.

18 citations

Journal ArticleDOI
TL;DR: A novel GNN model unifying the textual contents and interaction network for user geolocation prediction, MAGNN has the ability to capture multi-aspect information from multiple sources of data, which makes MAGNN inductive and easily adapt to few label scenarios.
Abstract: Identifying the geographical locations of online social media users, a.k.a. user geolocation (UG), is an essential task for many location-based applications such as advertising, social event detection, emergency localization, etc. Due to the unwillingness of revealing privacy information for most users, it is challenging to directly locate users with the ground-truth geotags. Recent efforts sidestep this limitation through retrieving users’ locations by alternatively unifying user generated contents (e.g., texts and public profiles) and online social relations. Though achieving some progress, previous methods rely on the similarity of texts and/or neighboring nodes for user geolocation, which suffers the problems of: (1) location-agnostic problem of network representation learning, which largely impedes the performance of their prediction accuracy; and (2) lack of interpretability w.r.t. the predicted results that is crucial for understanding model behavior and further improving prediction performance. To cope with such issues, we proposed a Multiple-aspect Attentional Graph Neural Networks (MAGNN) – a novel GNN model unifying the textual contents and interaction network for user geolocation prediction. The attention mechanism of MAGNN has the ability to capture multi-aspect information from multiple sources of data, which makes MAGNN inductive and easily adapt to few label scenarios. In addition, our model is able to provide meaningful explanations on the UG results, which is crucial for practical applications and subsequent decision makings. We conduct comprehensive evaluations over three real-world Twitter datasets. The experimental results verify the effectiveness of the proposed model compared to existing methods and shed lights on the interpretable user geolocation.

18 citations

Proceedings ArticleDOI
07 Oct 2004
TL;DR: The experiments of SPIE-DM indicate that the system is very promising for extracting and mining from biomedical literature databases.
Abstract: We present a biomedical literature data mining system SPIE-DM (Scalable and Portable Information Extraction and Data Mining) to extract and mine the protein-protein interaction network from biomedical literature such as MedLine SPIE-DM consists of two phases: in phase 1, we develop a scalable and portable ie method (SPIE) to extract the protein-protein interaction from the biomedical literature These extracted protein-protein interactions form a scale-free network graph In phase 2, we apply a novel clustering method SFCluster to mine the protein-protein interaction network The clusters in the network graph represent some potential protein complexes, which are very important for biologist to study the protein functionality The clustering algorithm considers the characteristics of the scale-free network graphs and is based on the local density of the vertex and its neighborhood functions that can be used to find more meaningful clusters at different density levels The experiments of SPIE-DM on around 1600 chromatin proteins indicate that our system is very promising for extracting and mining from biomedical literature databases

18 citations

Journal ArticleDOI
05 Mar 2019
TL;DR: A team led by María Rodríguez Martínez at IBM Research - Zürich has developed PIMKL, a methodology that exploits prior knowledge and enables the integration of multiple types of data with varying predictive power and produces a molecular signature that enables the interpretation of the results in terms of known biological functions.
Abstract: Reliable identification of molecular biomarkers is essential for accurate patient stratification. While state-of-the-art machine learning approaches for sample classification continue to push boundaries in terms of performance, most of these methods are not able to integrate different data types and lack generalization power, limiting their application in a clinical setting. Furthermore, many methods behave as black boxes, and we have very little understanding about the mechanisms that lead to the prediction. While opaqueness concerning machine behavior might not be a problem in deterministic domains, in health care, providing explanations about the molecular factors and phenotypes that are driving the classification is crucial to build trust in the performance of the predictive system. We propose Pathway-Induced Multiple Kernel Learning (PIMKL), a methodology to reliably classify samples that can also help gain insights into the molecular mechanisms that underlie the classification. PIMKL exploits prior knowledge in the form of a molecular interaction network and annotated gene sets, by optimizing a mixture of pathway-induced kernels using a Multiple Kernel Learning (MKL) algorithm, an approach that has demonstrated excellent performance in different machine learning applications. After optimizing the combination of kernels to predict a specific phenotype, the model provides a stable molecular signature that can be interpreted in the light of the ingested prior knowledge and that can be used in transfer learning tasks.

18 citations

Journal ArticleDOI
TL;DR: This paper proposes a novel generative model based SMCC algorithm, called GM-SMCC, to effectively compute the label probability distributions of unannotated protein instances and predict their functional properties and shows that the performances of the proposed algorithms are better than the other compared algorithms.
Abstract: With the rapid accumulation of proteomic and genomic datasets in terms of genome-scale features and interaction networks through high-throughput experimental techniques, the process of manual predicting functional properties of the proteins has become increasingly cumbersome, and computational methods to automate this annotation task are urgently needed. Most of the approaches in predicting functional properties of proteins require to either identify a reliable set of labeled proteins with similar attribute features to unannotated proteins, or to learn from a fully-labeled protein interaction network with a large amount of labeled data. However, acquiring such labels can be very difficult in practice, especially for multi-label protein function prediction problems. Learning with only a few labeled data can lead to poor performance as limited supervision knowledge can be obtained from similar proteins or from connections between them. To effectively annotate proteins even in the paucity of labeled data, it is important to take advantage of all data sources that are available in this problem setting, including interaction networks, attribute feature information, correlations of functional labels, and unlabeled data. In this paper, we show that the underlying nature of predicting functional properties of proteins using various data sources of relational data is a typical collective classification (CC) problem in machine learning. The protein functional prediction task with limited annotation is then cast into a semi-supervised multi-label collective classification (SMCC) framework. As such, we propose a novel generative model based SMCC algorithm, called GM-SMCC, to effectively compute the label probability distributions of unannotated protein instances and predict their functional properties. To further boost the predicting performance, we extend the method in an ensemble manner, called EGM-SMCC, by utilizing multiple heterogeneous networks with various latent linkages constructed to explicitly model the relationships among the nodes for effectively propagate the supervision knowledge from labeled to unlabeled nodes. Experimental results on a yeast gene dataset predicting the functions and localization of proteins demonstrate the effectiveness of the proposed method. In the comparison, we find that the performances of the proposed algorithms are better than the other compared algorithms.

18 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202337
202290
2021183
2020221
2019201
2018163