scholarly journals TREND PREDICTION IN TEMPORAL BIPARTITE NETWORKS: THE CASE OF MOVIELENS, NETFLIX, AND DIGG

2013 ◽  
Vol 16 (04n05) ◽  
pp. 1350024 ◽  
Author(s):  
AN ZENG ◽  
STANISLAO GUALDI ◽  
MATÚŠ MEDO ◽  
YI-CHENG ZHANG
Keyword(s):  
Social Network ◽  
Prediction Performance ◽  
Prediction Methods ◽  
Bipartite Networks ◽  
Trend Prediction ◽  
Online Services ◽  
Online Systems ◽  
Near Future

Online systems, where users purchase or collect items of some kind, can be effectively represented by temporal bipartite networks where both nodes and links are added with time. We use this representation to predict which items might become popular in the near future. Various prediction methods are evaluated on three distinct datasets originating from popular online services (Movielens, Netflix, and Digg). We show that the prediction performance can be further enhanced if the user social network is known and centrality of individual users in this network is used to weight their actions.

Basic Study on Trend Prediction for Style Design

2008 ◽  
Author(s):  
Shinya Yoshida ◽  
Hideki Aoyama
Keyword(s):  
Product Design ◽  
Least Square Method ◽  
Least Square ◽  
Concept Design ◽  
Trend Prediction ◽  
Basic Study ◽  
Moving Least Square ◽  
And Function ◽  
Eigenvalue And Eigenvector ◽  
Near Future

With diversification of consumer taste, appearance shape together with functionality contributes to the appeal of a product vastly. Concept design and industrial design therefore serve as an important process in product development. These designs are difficult to perform based on theoretical backing, since appearance shape design is a creative activity which depends on a designer’s aesthetic sense strongly. When embodying a product shape, naturally design is determined not only by a designer’s sensitivity but by use and function of a product as well. It is also important to investigate designs desired by consumers, and reflect all of this in the product design. The ability to predict consumer taste trends therefore greatly aids product design. In this research, the prototype models of a product in trend every year were made by multiplying weights according to the number of a product sold in the past to calculate that the rate of exaggeration of prototype models of each year to all whole prototype models. The straight extrapolation of the Spline method was applied to the exaggeration vector, and the technique of predicting shapes preferred by consumers in the near future using that method was proposed. Moreover the eigenspace method was applied to similar product shapes to propose the technique of grasping the features of shape for every year by computing the eigenvalue and eigenvector of the coordinates of the points of the shapes as well as the technique of predicting shapes which consumers will prefer in the near future by using the Linear function of Moving Least Square method.

scholarly journals Innovation in the sphere of advertising: latest innovations and prospects of development

2021 ◽  
Vol 12 (2) ◽  
pp. 140-146
Author(s):  
Мария Олеговна Сураева ◽  
Maria O. Suraeva
Keyword(s):  
Information Technology ◽  
Social Network ◽  
Digital Marketing ◽  
Outdoor Advertising ◽  
The Past ◽  
New Methods ◽  
Contextual Advertising ◽  
Product Value ◽  
Fast Pace ◽  
Near Future

Advertising innovation is growing at a fast pace today. We observe how advertising is becoming more and more digital, it has also become easier to use, now it can be done not only by an agency, but by any person who has one or another category of listeners, an audience in a social network. But it should be noted that digital advertising still does not completely replace outdoor advertising, which has undergone dramatic changes over the past few years: new creative ways of presenting product value to the consumer have appeared, often using information technology. This article discusses the main innovations in the field of digital and outdoor advertising over the past five years, as well as the prospects for their development in the near future. The article reveals new methods of communication between the seller and the client using advertising, the reasons for using contextual advertising (safety, efficiency), reflects how to correctly apply the technologies of innovative types of advertising today and how companies understand what the consumer needs and why they need to focus on social and digital marketing. It also analyzed what factors should be considered when choosing advertising methods.

Prediction of missing links and reconstruction of complex networks

2016 ◽  
Vol 27 (10) ◽  
pp. 1650120 ◽  
Author(s):  
Cheng-Jun Zhang ◽  
An Zeng
Keyword(s):  
Complex Networks ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
High Performance ◽  
Point Of View ◽  
Prediction Methods ◽  
Online Systems ◽  
Network Properties ◽  
High Prediction ◽  
Simple Link

Predicting missing links in complex networks is of great significance from both theoretical and practical point of view, which not only helps us understand the evolution of real systems but also relates to many applications in social, biological and online systems. In this paper, we study the features of different simple link prediction methods, revealing that they may lead to the distortion of networks’ structural and dynamical properties. Moreover, we find that high prediction accuracy is not definitely corresponding to a high performance in preserving the network properties when using link prediction methods to reconstruct networks. Our work highlights the importance of considering the feedback effect of the link prediction methods on network properties when designing the algorithms.

scholarly journals Geographic impressions in Facebook political ads

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Adina Gitomer ◽  
Pavel V. Oleinikov ◽  
Laura M. Baum ◽  
Erika Franklin Fowler ◽  
Saray Shai
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Political Advertising ◽  
Geographic Region ◽  
Political Campaigns ◽  
Bipartite Networks ◽  
Computational Tools ◽  
Meta Data ◽  
Geographic Concentration ◽  
Wide Range

AbstractOnline political advertising is becoming increasingly popular as political campaigns recognize the utility of social network platforms, like Facebook, for reaching and engaging with voters. Yet, contrary to the wealth of information about campaign advertising on TV, little is known about advertising online, as comprehensive data only recently became available to scholars. Moreover, the newly available data is often aggregated, incomplete, and imprecise. Here, we present an analysis of Facebook political ad data, supplemented with funding-related meta-data obtained through human coding and a partnership with the Center for Responsive Politics. Through computational tools—namely, network analysis—we aim to use this data to describe and categorize political ad funding behavior on Facebook. Specifically, we focus on the geographic concentration of ads, and discover that most ads reach an audience in a single geographic region (i.e., U.S. state) or in a wide range of regions, and very few reach an audience spanning a small number of regions. We use this observation to partition funding entities into three groups based on their relationships to regionally-concentrated ads. We then examine the differences between these groups via bipartite networks connecting funding entities to their geographic audiences, as well as content they support. Our findings reveal that geographic impressions play an important role in online political advertising, and can be used to classify funding entities. As a result, this study represents a step toward ensuring political funding transparency and demystifying online political advertising more broadly.

Big data implementation for price statistics in Indonesia: Past, current, and future developments

2021 ◽  
pp. 1-13
Author(s):  
Setia Pramana ◽  
Siti Mariyah ◽  
Takdir
Keyword(s):  
Big Data ◽  
Online Shopping ◽  
Rapid Development ◽  
Data Sources ◽  
Massive Data ◽  
It Infrastructure ◽  
Online Systems ◽  
Future Developments ◽  
Near Future ◽  
Future Plans

The rapid development of Big Data as result of increasing interactivity with online systems between humans (e.g., online shopping, marketplace) and machine (internet of things, mobile phone, etc.) has led to a measurement revolution. This massive data if being mined and analyzed correctly can provide valuable alternative data sources for official statistics, especially price statistics. Several studies for using diverse Big Data as new sources of price statistics in Indonesia have been initiated. This article would provide a comprehensive review of experiences in exploiting various Big Data sources for price statistics, followed by the current development and the near future plans. The development of system and IT infrastructure is also discussed. Based on this experience, limitations, challenges, and advances for each approach would be presented.

scholarly journals Improving Business Pages Recommendation in Social Network Using Link Prediction Methods

2021 ◽  
pp. 1-12
Author(s):  
Watare Asaph ◽  
Shaowei Sun
Keyword(s):  
Social Network ◽  
Link Prediction ◽  
Area Under Curve ◽  
Prediction Models ◽  
Modern Society ◽  
Prediction Methods ◽  
Baseline Model ◽  
Exchange Information ◽  
Internet Link ◽  
A Company

Recently Social Network has become one of the favorite means for a modern society to perform social interaction and exchange information via the internet. Link prediction is a common problem that has broad application in such social networks, ranging from predicting unobserved interaction to recommending related items. In this paper, we investigate link recommendations over business pages on Facebook Social Network. More specifically, given a company in thenetwork, we want to recommend potential companies to connect with. We start by introducing existing work in link recommendations and some link prediction models as our baseline. We then talk about the Graph Neural Network model SEAL to make a link recommendations in the network. Our results show that SEAL outperformed the compared baseline model while reaching above 94% Area Under Curve accuracy in link recommendations.

scholarly journals An Experimental Evaluation of Similarity-Based and Embedding-Based Link Prediction Methods on Graphs

2021 ◽  
Vol 11 (5) ◽  
pp. 1-18
Author(s):  
Md Kamrul Islam ◽  
Sabeur Aridhi ◽  
Malika Smail-Tabbone
Keyword(s):  
Link Prediction ◽  
Graph Embedding ◽  
Black Box ◽  
Prediction Performance ◽  
Prediction Methods ◽  
Good Prediction ◽  
Good Prediction Performance ◽  
Node Similarity ◽  
Category Comparison ◽  
Selection Of

The task of inferring missing links or predicting future ones in a graph based on its current structure is referred to as link prediction. Link prediction methods that are based on pairwise node similarity are well-established approaches in the literature and show good prediction performance in many realworld graphs though they are heuristic. On the other hand, graph embedding approaches learn lowdimensional representation of nodes in graph and are capable of capturing inherent graph features, and thus support the subsequent link prediction task in graph. This paper studies a selection of methods from both categories on several benchmark (homogeneous) graphs with different properties from various domains. Beyond the intra and inter category comparison of the performances of the methods, our aim is also to uncover interesting connections between Graph Neural Network(GNN)- based methods and heuristic ones as a means to alleviate the black-box well-known limitation.

A supervised similarity measure for link prediction based on KNN

2021 ◽  
pp. 2150112
Author(s):  
Longjie Li ◽  
Hui Wang ◽  
Shiyu Fang ◽  
Na Shan ◽  
Xiaoyun Chen
Keyword(s):  
Link Prediction ◽  
Nearest Neighbors ◽  
Structural Features ◽  
Prediction Performance ◽  
Prediction Methods ◽  
Node Pair ◽  
Structure Information ◽  
Local Mean ◽  
Mean Vectors ◽  
Negative Formula

As a research hotspot of complex network analysis, link prediction has received growing attention from various disciplines. Link prediction intends to determine the connecting probability of latent links based on the observed structure information. To this end, a host of similarity-based and learning-based link prediction methods have been proposed. To attain stable prediction performance on diverse networks, this paper proposes a supervised similarity-based method, which absorbs the advantages of both kinds of link prediction methods. In the proposed method, to capture the characteristics of a node pair, a collection of structural features is extracted from the network to represent the node pair as a vector. Then, the positive and negative [Formula: see text]-nearest neighbors are searched from existing and nonexisting links, respectively. The connection likelihood of a node pair is measured according to its distances to the local mean vectors of positive and negative [Formula: see text]-nearest neighbors. The prediction performance of the proposed method is experimentally evaluated on 10 benchmark networks. The results show that the proposed method is superior to the compared methods in terms of accuracy and stableness.

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