Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Relations versus Near Neighbours: Reliable Recommenders in Limited Information Social Network Collaborative Filtering for Online Advertising

Online advertising benefits by recommender systems since the latter analyse reviews and rating of products, providing useful insight of the buyer perception of products and services. When traditional recommender system information is enriched with social network information, more successful recommendations are produced, since more users' aspects are taken into consideration. However, social network information may be unavailable since some users may not have social network accounts or may not consent to their use for recommendations, while rating data may be unavailable due to the cold start phenomenon. In this paper, we propose an algorithm that combines limited collaborative filtering information, comprised only of users' ratings on items, with limited social network information, comprised only of users' social relations, in order to improve (1) prediction accuracy and (2) prediction coverage in collaborative filtering recommender systems, at the same time. The proposed algorithm considerably improves rating prediction accuracy and coverage, while it can be easily integrated in recommender systems.
A Comparison of Methods for Link Sign Prediction with Signed Network Embeddings

In many real-world networks, it is important to explicitly differentiate between positive and negative links, thus considering the observed networks as signed. To derive useful features, just as in the case of unsigned networks, representation learning can be used to learn meaningful representations of a network that characterize its underlying topology. Several methods for learning representations on signed networks have already been proposed but have not been systematically benchmarked together before. Hence, in this paper, we bridge this literature gap providing a quantitative and qualitative benchmark of the four most prominent representation learning methods for signed networks. Results on three different datasets for link sign prediction showcase the superiority of the StEM method over its competitors both from a predictive performance and runtime perspective.
Becoming Gatekeepers Together with Allies: Collaborative Brokerage over Social Networks

Information brokers control information flow and hold dominating positions in a social network. We study how a team of individuals with heterogeneous influencing power may gain such advantageous position through establishing new links. In particular, a collaborative brokerage problem aims to find the smallest set of nodes for a team of individuals with different influencing power to cover the entire network. We phrase this problem as an extension to the classical graph domination problem and thus this problem is NP-hard. We show that a polynomial-time solution exists for directed trees. We then develop efficient algorithms over arbitrary directed networks. To evaluate the algorithms, we run experiments over networks generated using well-known random graph models and real-world datasets. Experimental results show that our algorithms produce relatively good solutions with faster speed.
Meta-GNN: Metagraph Neural Network for Semi-supervised learning in Attributed Heterogeneous Information Networks

Heterogeneous Information Networks (HINs) comprise nodes of different types inter-connected through diverse semantic relationships. In many real-world applications, nodes in information networks are often associated with additional attributes, resulting in Attributed HINs (or AHINs). In this paper, we study semi-supervised learning (SSL) on AHINs to classify nodes based on their structure, node types and attributes, given limited supervision. Recently, Graph Convolutional Networks (GCNs) have achieved impressive results in several graph-based SSL tasks. However, they operate on homogeneous networks, while being completely agnostic to the semantics of typed nodes and relationships in real-world HINs. In this paper, we seek to bridge the gap between semantic-rich HINs and the neighborhood aggregation paradigm of graph neural networks, to generalize GCNs through metagraph semantics. We propose a novel metagraph convolution operation to extract features from local metagraph-structured neighborhoods, thus capturing semantic higher-order relationships in AHINs. Our proposed neural architecture Meta-GNN extracts features of diverse semantics by utilizing multiple metagraphs, and employs a novel metagraph-attention module to learn personalized metagraph preferences for each node. Our semi-supervised node classification experiments on multiple real-world AHIN datasets indicate significant performance gains of 6% Micro-F1 on average over state-of-the-art AHIN baselines. Visualizations on metagraph attention weights yield interpretable insights into their relative task-specific importance.
RumorSleuth: Joint Detection of Rumor Veracity and User Stance

The penetration of social media has had deep and far-reaching consequences in information production and consumption. Widespread use of social media platforms has engendered malicious users and attention seekers to spread rumors and fake news. This trend is particularly evident in various microblogging platforms where news becomes viral in a matter of hours and can lead to mass panic and confusion. One intriguing fact regarding rumors and fake news is that very often rumor stories prompt users to adopt different stances about the rumor posts. Understanding user stances in rumor posts is thus very important to identify the veracity of the underlying content. While rumor veracity and stance detection have been viewed as disjoint tasks we demonstrate here how jointly learning both of them can be fruitful. In this paper, we propose RumorSleuth, a multitask deep learning model which can leverage both the textual information and user profile information to jointly identify the veracity of a rumor along with users' stances. Tests on two publicly available rumor datasets demonstrate that RumorSleuth outperforms current state-of-the-art models and achieves up to 14% performance gain in rumor veracity classification and around 6% improvement in user stance classification.
On Augmented Identifying Codes for Monitoring Drug Trafficking Organizations

A staggering 450,000 people died due to drug consumption in 2015, out of which, a third of the deaths were a direct result of drug overdosing. Illicit manufacturing of Cocaine, Heroin, Cannabis, etc., by Drug Trafficking Organizations (DTOs), all peaked recently, which is a major indication of their worldwide demand. With drug offenses increasing globally, the list of suspect individuals, associated with drug trafficking organizations, has also been growing over the past few decades. As it takes significant amount of technical and human resources to monitor a suspect, an increasing list entails greater resource requirements on the part of law enforcement agencies. Soon, monitoring all the suspects on the list becomes an impossible task. In this paper, we present a novel methodology called Augmented Identifying Codes (AIC), an extension of the mathematical notion of Identifying Codes. We show that our method requires significantly lesser resources, on the part of the law enforcement agencies, when compared to strategies adopting standard network centrality measures, for monitoring of individuals associated with drug trafficking organizations. Finally, we evaluate the efficacy of our approach on real world datasets.
Infinite Motif Stochastic Blockmodel for Role Discovery in Networks

Role/block discovery is an essential task in network analytics so it has attracted significant attention recently. Previous studies on role discovery either relied on first or second-order structural information to group nodes but neglected the higherorder information or required the number of roles/blocks as the input which may be unknown in practice. To overcome these limitations, in this paper we propose a novel generative model, infinite motif stochastic blockmodel (IMM), for role discovery in networks. IMM takes advantage of high-order motifs in the generative process and it is a nonparametric Bayesian model which can automatically infer the number of roles. To validate the effectiveness of IMM, we conduct experiments on synthetic and real-world networks. The obtained results demonstrate IMM outperforms other blockmodels in role discovery task.
Travel Routes Recommendations via Online Social Networks

On line social networks (e.g., Facebook, Twitter) allow users to tag their posts with geographical coordinates collected through the GPS interface of smart phones. The time- and geo-coordinates associated with a sequence of tweets manifest the spatial-temporal movements of people in real life. The paper presents an approach to recommend travel routes to social media users exploiting historic mobility data, social features of users and geographic characteristics of locations. Travel routes recommendation is formulated as a ranking problem aiming at minimg the top interesting locations and travel sequences among them, and exploit such information to recommend the most suitable travel routes to a target user. A ranking function that exploits users' similarity in visiting locations and in travelling along mobility paths is used to predict places the user could like. The experimental results obtained by using a real-world dataset of tweets show that the proposed method is effective in recommending travel routes achieving remarkable precision and recall rates.
Topic Enhanced Word Embedding for Toxic Content Detection in Q&A Sites

Increasingly, users are adopting community question-and-answer (Q&A) sites to exchange information. Detecting and eliminating toxic and divisive content in these Q&A sites are paramount tasks to ensure a safe and constructive environment for the users. Insincere question, which is founded upon false premises, is one type of toxic content in Q&A sites. In this paper, we proposed a novel deep learning framework enhanced pre-trained word embeddings with topical information for insincere question classification. We evaluated our proposed framework on a large real-world dataset from Quora Q&A site and showed that the topically enhanced word embedding is able to achieve better results in toxic content classification. An empirical study was also conducted to analyze the topics of the insincere questions on Quora, and we found that topics on “religion”, “gender” and ‘'politics'’ has a higher proportion of insincere questions.
How the Tables Have Turned: Studying the New Wave of Social Bots on Twitter Using Complex Network Analysis Techniques

Twitter bots have evolved from easily-detectable, simple content spammers with bogus identities to sophisticated players embedded in deep levels of social networks, silently promoting affiliate campaigns, marketing various products and services, and orchestrating or coordinating political activities. Much research has been reported on building accurate machine learning classifiers to identifying bots in social networks; recent works on social bots have started the new line of research on the existence, placement, and functions of the bots in a collective manner. In this paper, we study two families of Twitter bots which have been studied previously with respect to spamming activities through advertisement and political campaigns, and perform an evolutionary comparison with the new waves of bots currently found in Twitter. We uncover various evolved tendencies of the new social bots under social, communication, and behavioral patterns. Our findings show that these bots demonstrate evolved core-periphery structure; are deeply embedded in their networks of communication; exhibit complex information diffusion and heterogeneous content authoring patterns; perform mobilization of leaders across communication roles; and reside in niche topic communities. These characteristics make them highly deceptive as well as more effective in achieving operational goals than their traditional counterparts. We conclude by discussing some possible applications of the discovered behavioral and social traits of the evolved bots, and ways to build effective bot detection systems.
Knowledge Embedding towards the Recommendation with Sparse User-Item Interactions

Recently, many researchers in recommender systems have realized that encoding user-item interactions based on deep neural networks (DNNs) promotes collaborative-filtering (CF)'s performance. Nonetheless, those DNN-based models' performance is still limited when observed user-item interactions are very less because the training samples distilled from these interactions are critical for deep learning models. To address this problem, we resort to plenty features distilled from knowledge graphs (KGs), to profile users and items precisely and sufficiently rather than observed user-item interactions. In this paper, we propose a knowledge embedding based recommendation framework to alleviate the problem of sparse user-item interactions in recommendation. In our framework, each user and each item are both represented by the combination of an item embedding and a tag embedding at first. Specifically, item embeddings are learned by Metapath2Vec which is a graph embedding model qualified to embedding heterogeneous information networks. Tag embeddings are learned by a Skip-gram model similar to word embedding. We regarded these embeddings as knowledge embeddings because they both indicate knowledge about the latent relationships of movie-movie and user-movie. At last, a target user's representation and a candidate movie's representation are both fed into a multi-layer perceptron to output the probability that the user likes the item. The probability can be further used to achieve top-n recommendation. The extensive experiments on a movie recommendation dataset demonstrate our framework's superiority over some state-of-the-art recommendation models, especially in the scenario of sparse user-movie interactions.
Increasing the Diffusional Characteristics of Networks Through Optimal Topology Changes within Sub-graphs

In recent years, bustling online communities have focused a lot of attention on research dealing with information spreading. Through acquired knowledge about the characteristics of information spreading processes, we are able to influence their dynamics via the enhancement of propagation properties or by changing them to decrease their spread within a network. One of approaches is adding or removing connections within a network. While optimal linking within complex networks requires extensive computational resources, in this investigation, we focus on the optimization of the topology of small graphs within larger network structures. The study shows how the enhancement of propagation properties within small networks is preserved in bigger networks based on connected smaller graphs. We compare the results from combined small graphs with added links providing optimal spread and networks with additional random linking. The results show that improvements in linking within small sub-graphs with optimal linking improves the diffusional properties of the whole network.
Through The Eyes of A Poet: Classical Poetry Recommendation with Visual Input on Social Media

With the increasing popularity of portable devices with cameras (e.g., smartphones and tablets) and ubiquitous Internet connectivity, travelers can share their instant experience during the travel by posting photos they took to social media platforms. In this paper, we present a new image-driven poetry recommender system that takes a traveler's photo as input and recommends classical poems that can enrich the photo with aesthetically pleasing quotes from the poems. Three critical challenges exist to solve this new problem: i) how to extract the implicit artistic conception embedded in both poems and images? ii) How to identify the salient objects in the image without knowing the creator's intent? iii) How to accommodate the diverse user perceptions of the image and make a diversified poetry recommendation? The proposed iPoemRec system jointly addresses the above challenges by developing heterogeneous information network and neural embedding techniques. Evaluation results from real-world datasets and a user study demonstrate that our system can recommend highly relevant classical poems for a given photo and receive significantly higher user ratings compared to the state-of-the-art baselines.
Assigning Users to Domains of Interest Based on Content and Network Similarity with Champion Instances

In this paper, we propose two approaches to the problem of finding similar users to a set of champions representing domains of interest on social media. The first approach is based on the content shared by the users, while the second one relies on the social network connections (following, followers, and mentions). Given a small set of champion accounts, we construct a centroid and we rank candidates by computing their distance from the centroid. Experiments show that social network features provide better performance, but they are computationally much more intensive. This approach can be used for providing highly reliable recommendations of the top-k instances which are most similar to a given target, specified through examples rather than through specific properties.
Location, Location, Location! Quantifying the True Impact of Location on Business Reviews Using a Yelp Dataset

Today, with the emergence of various business review sites such as Yelp, Trip Advisor, and Zomato, people can write reviews and provide an assessment (often as 1–5 score rating). The success of a business on the crowd-sourced review platform has taken the form of positive reviews and high star ratings (failure are associated with negative reviews and low star ratings). We often claim that location plays a major role in determining the success or the failure of a given business. This paper attempts to verify this claim and quantifies the impact of location, solely, on business success, using two data sets; a Yelp dataset for business information and reviews, and another Location dataset that gathers location-based information in a city or an area. We perform an empirical study to quantify the impact of (i) relative location to well known landmarks and (ii) parameterized location (such as cost of living in a given zip code), on the success of restaurants. In our study, we found that parameterized location using location characteristic parameters such as housing affordability correlate highly with restaurant success with more than 0.81 correlation ratio. We also observe that the closer the restaurant to a landmark (relative location) the more likelihood it succeeds.
Multivariate Motif Detection in Local Weather Big Data

In recent years, there are very frequent reports of disasters attributed to the climate change and there are several reports that these extreme phenomena will further affect people not only as weather disasters but also indirectly with the shortage of natural resources such as water or food due to the climate change. Towards this direction, there is an on-going research that studies weather phenomena by collecting data not only in the surface of the globe but also at the different levels of the atmosphere. Having such a large volume of data, traditional numerical weather prediction models may not be able to assimilate those data and extract knowledge useful for the prediction of extreme phenomena. Thus, analysis of weather data has been transformed into a big data analytics problem which may enable weather scientists to better understand the interrelations of the weather variables and use the knowledge discovered to improve their prediction models. In this context, the current paper proposes a big data analytics methodology that is able to detect all common patterns between different weather variables in neighboring or distant points in a specific time window revealing useful associations between weather variables which is not possible to detect otherwise with the traditional numerical methods. The proposed methodology is based on a data structure that is able to store the magnitude of the weather data in different dimensions and a pattern detection algorithm which is able to detect all common patterns. The experimental results using weather data from the National Oceanic and Atmospheric Administration (NOAA) revealed interesting otherwise unknown patterns in two weather variables for two specific locations that were studied.
On Churn and Social Contagion

Massively Multiplayer Online Role-Playing Games (MMORPGs) are persistent virtual environments where millions of players interact in an online manner. We study the problem of player churn and social contagion using MMORPG game logs by analyzing the impact of a node's churn behavior on its immediate neighborhood or group. The two key research questions in this paper are - When an active node, ego, becomes dormant, what is the impact on the activity behavior of ego's immediate neighbor, alter, 1) based on ego's characteristics and ego's relationship with alter and 2) based on the activity behavior of alter's remaining neighbors. We use a supervised learning framework to study the impact of player churn and social contagion. Experimental results show that the classification models perform substantially better than random for both the research problems. Finally, we use a data-driven approach to propose a player typology based on degree of socialization and analyze churn behavior among these player types. Experimental results show that the loner player type is much more likely to churn than the socializer player types and as the degree of socialization decreases among socializers, the propensity to churn increases.