scholarly journals Mobile App Start-Up Prediction Based on Federated Learning and Attributed Heterogeneous Network Embedding

2021 ◽  
Vol 13 (10) ◽  
pp. 256
Author(s):  
Shaoyong Li ◽  
Liang Lv ◽  
Xiaoya Li ◽  
Zhaoyun Ding
Keyword(s):  
Heterogeneous Network ◽  
Prediction Method ◽  
Cold Start ◽  
Mobile App ◽  
Network Embedding ◽  
User Privacy ◽  
Start Up ◽  
User Data ◽  
Single User ◽  
Cold Start Problem

At present, most mobile App start-up prediction algorithms are only trained and predicted based on single-user data. They cannot integrate the data of all users to mine the correlation between users, and cannot alleviate the cold start problem of new users or newly installed Apps. There are some existing works related to mobile App start-up prediction using multi-user data, which require the integration of multi-party data. In this case, a typical solution is distributed learning of centralized computing. However, this solution can easily lead to the leakage of user privacy data. In this paper, we propose a mobile App start-up prediction method based on federated learning and attributed heterogeneous network embedding, which alleviates the cold start problem of new users or new Apps while guaranteeing users’ privacy.

scholarly journals A network embedding model for pathogenic genes prediction by multi-path random walking on heterogeneous network

2019 ◽  
Vol 12 (S10) ◽  
Author(s):  
Bo Xu ◽  
Yu Liu ◽  
Shuo Yu ◽  
Lei Wang ◽  
Jie Dong ◽  
...  
Keyword(s):  
Heterogeneous Network ◽  
Structural Information ◽  
Single Gene ◽  
Prediction Method ◽  
Experimental Results ◽  
Network Embedding ◽  
Pathogenic Genes ◽  
Phenotype Data ◽  
Multiple Paths ◽  
Complex Structural

Abstract Background Prediction of pathogenic genes is crucial for disease prevention, diagnosis, and treatment. But traditional genetic localization methods are often technique-difficulty and time-consuming. With the development of computer science, computational biology has gradually become one of the main methods for finding candidate pathogenic genes. Methods We propose a pathogenic genes prediction method based on network embedding which is called Multipath2vec. Firstly, we construct an heterogeneous network which is called GP−network. It is constructed based on three kinds of relationships between genes and phenotypes, including correlations between phenotypes, interactions between genes and known gene-phenotype pairs. Then in order to embedding the network better, we design the multi-path to guide random walk in GP−network. The multi-path includes multiple paths between genes and phenotypes which can capture complex structural information of heterogeneous network. Finally, we use the learned vector representation of each phenotype and protein to calculate the similarities and rank according to the similarities between candidate genes and the target phenotype. Results We implemented Multipath2vec and four baseline approaches (i.e., CATAPULT, PRINCE, Deepwalk and Metapath2vec) on many-genes gene-phenotype data, single-gene gene-phenotype data and whole gene-phenotype data. Experimental results show that Multipath2vec outperformed the state-of-the-art baselines in pathogenic genes prediction task. Conclusions We propose Multipath2vec that can be utilized to predict pathogenic genes and experimental results show the higher accuracy of pathogenic genes prediction.

scholarly journals DTI-HeNE: a novel method for drug-target interaction prediction based on heterogeneous network embedding

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yang Yue ◽  
Shan He
Keyword(s):  
Machine Learning ◽  
Heterogeneous Network ◽  
Drug Target ◽  
Prediction Method ◽  
Drug Repurposing ◽  
Graph Embedding ◽  
Multiple Drug ◽  
Network Embedding ◽  
Protein Targets ◽  
Target Interaction

Abstract Background Prediction of the drug-target interaction (DTI) is a critical step in the drug repurposing process, which can effectively reduce the following workload for experimental verification of potential drugs’ properties. In recent studies, many machine-learning-based methods have been proposed to discover unknown interactions between drugs and protein targets. A recent trend is to use graph-based machine learning, e.g., graph embedding to extract features from drug-target networks and then predict new drug-target interactions. However, most of the graph embedding methods are not specifically designed for DTI predictions; thus, it is difficult for these methods to fully utilize the heterogeneous information of drugs and targets (e.g., the respective vertex features of drugs and targets and path-based interactive features between drugs and targets). Results We propose a DTI prediction method DTI-HeNE (DTI based on Heterogeneous Network Embedding), which is specifically designed to cope with the bipartite DTI relations for generating high-quality embeddings of drug-target pairs. This method splits a heterogeneous DTI network into a bipartite DTI network, multiple drug homogeneous networks and target homogeneous networks, and extracts features from these sub-networks separately to better utilize the characteristics of bipartite DTI relations as well as the auxiliary similarity information related to drugs and targets. The features extracted from each sub-network are integrated using pathway information between these sub-networks to acquire new features, i.e., embedding vectors of drug-target pairs. Finally, these features are fed into a random forest (RF) model to predict novel DTIs. Conclusions Our experimental results show that, the proposed DTI network embedding method can learn higher-quality features of heterogeneous drug-target interaction networks for novel DTIs discovery.

Tracing or Tracking? A Preliminary Analysis of COVID-19 Outbreak Tracker Apps on Android and User Privacy (Preprint)

2020 ◽  
Author(s):  
Alex Akinbi ◽  
Ehizojie Ojie
Keyword(s):  
Preliminary Analysis ◽  
Service Providers ◽  
User Participation ◽  
Memory Storage ◽  
Phone Call ◽  
Contact Tracing ◽  
Privacy Rights ◽  
User Privacy ◽  
Android Apps ◽  
User Data

BACKGROUND Technology using digital contact tracing apps has the potential to slow the spread of COVID-19 outbreaks by recording proximity events between individuals and alerting people who have been exposed. However, there are concerns about the abuse of user privacy rights as such apps can be repurposed to collect private user data by service providers and governments who like to gather their citizens’ private data. OBJECTIVE The objective of our study was to conduct a preliminary analysis of 34 COVID-19 trackers Android apps used in 29 individual countries to track COVID-19 symptoms, cases, and provide public health information. METHODS We identified each app’s AndroidManifest.xml resource file and examined the dangerous permissions requested by each app. RESULTS The results in this study show 70.5% of the apps request access to user location data, 47% request access to phone activities including the phone number, cellular network information, and the status of any ongoing calls. 44% of the apps request access to read from external memory storage and 2.9% request permission to download files without notification. 17.6% of the apps initiate a phone call without giving the user option to confirm the call. CONCLUSIONS The contributions of this study include a description of these dangerous permissions requested by each app and its effects on user privacy. We discuss principles that must be adopted in the development of future tracking and contact tracing apps to preserve the privacy of users and show transparency which in turn will encourage user participation.

scholarly journals EXPRESS: Overcoming the Cold Start Problem of CRM using a Probabilistic Machine Learning Approach

2021 ◽  
pp. 002224372110329
Author(s):  
Nicolas Padilla ◽  
Eva Ascarza
Keyword(s):  
Machine Learning ◽  
Cold Start ◽  
Customer Relationship ◽  
Exponential Families ◽  
Modeling Framework ◽  
Wide Range ◽  
The Moment ◽  
Probabilistic Machine Learning ◽  
Marketing Actions ◽  
Cold Start Problem

The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to identify and leverage differences across customers — a very diffcult task when firms attempt to manage new customers, for whom only the first purchase has been observed. For those customers, the lack of repeated observations poses a structural challenge to inferring unobserved differences across them. This is what we call the “cold start” problem of CRM, whereby companies have difficulties leveraging existing data when they attempt to make inferences about customers at the beginning of their relationship. We propose a solution to the cold start problem by developing a probabilistic machine learning modeling framework that leverages the information collected at the moment of acquisition. The main aspect of the model is that it exibly captures latent dimensions that govern the behaviors observed at acquisition as well as future propensities to buy and to respond to marketing actions using deep exponential families. The model can be integrated with a variety of demand specifications and is exible enough to capture a wide range of heterogeneity structures. We validate our approach in a retail context and empirically demonstrate the model's ability at identifying high-value customers as well as those most sensitive to marketing actions, right after their first purchase.

scholarly journals MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Léo Pio-Lopez ◽  
Alberto Valdeolivas ◽  
Laurent Tichit ◽  
Élisabeth Remy ◽  
Anaïs Baudot
Keyword(s):  
Heterogeneous Networks ◽  
Heterogeneous Network ◽  
Link Prediction ◽  
Network Embedding ◽  
Multiplex Networks ◽  
Multiplex Network ◽  
Gene Associations ◽  
Different Types ◽  
Embedding Methods ◽  
Node Embeddings

AbstractNetwork embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these approaches have demonstrated their effectiveness in tasks such as community detection, node classification, and link prediction. However, very few network embedding methods have been specifically designed to handle multiplex networks, i.e. networks composed of different layers sharing the same set of nodes but having different types of edges. Moreover, to our knowledge, existing approaches cannot embed multiple nodes from multiplex-heterogeneous networks, i.e. networks composed of several multiplex networks containing both different types of nodes and edges. In this study, we propose MultiVERSE, an extension of the VERSE framework using Random Walks with Restart on Multiplex (RWR-M) and Multiplex-Heterogeneous (RWR-MH) networks. MultiVERSE is a fast and scalable method to learn node embeddings from multiplex and multiplex-heterogeneous networks. We evaluate MultiVERSE on several biological and social networks and demonstrate its performance. MultiVERSE indeed outperforms most of the other methods in the tasks of link prediction and network reconstruction for multiplex network embedding, and is also efficient in link prediction for multiplex-heterogeneous network embedding. Finally, we apply MultiVERSE to study rare disease-gene associations using link prediction and clustering. MultiVERSE is freely available on github at https://github.com/Lpiol/MultiVERSE.

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