We survey the several measures that exists in literature to rank influential users in Twitter network.We propose a classification of these measures according to different criteria, such as the kind of metrics, the use of PageRank algorithm, the use of content analysis, among others.Besides the influential users, we also study measures of activity and popularity.We finish by mentioning some aspects of this topic related with computational complexity and correlation measures. Centrality is one of the most studied concepts in social network analysis. There is a huge literature regarding centrality measures, as ways to identify the most relevant users in a social network. The challenge is to find measures that can be computed efficiently, and that can be able to classify the users according to relevance criteria as close as possible to reality. We address this problem in the context of the Twitter network, an online social networking service with millions of users and an impressive flow of messages that are published and spread daily by interactions between users. Twitter has different types of users, but the greatest utility lies in finding the most influential ones. The purpose of this article is to collect and classify the different Twitter influence measures that exist so far in literature. These measures are very diverse. Some are based on simple metrics provided by the Twitter API, while others are based on complex mathematical models. Several measures are based on the PageRank algorithm, traditionally used to rank the websites on the Internet. Some others consider the timeline of publication, others the content of the messages, some are focused on specific topics, and others try to make predictions. We consider all these aspects, and some additional ones. Furthermore, we include measures of activity and popularity, the traditional mechanisms to correlate measures, and some important aspects of computational complexity for this particular context.

Measuring user influence on Twitter

We study the problem of correlating micro-blogging activity with stock-market events, defined as changes in the price and traded volume of stocks. Specifically, we collect messages related to a number of companies, and we search for correlations between stock-market events for those companies and features extracted from the micro-blogging messages. The features we extract can be categorized in two groups. Features in the first group measure the overall activity in the micro-blogging platform, such as number of posts, number of re-posts, and so on. Features in the second group measure properties of an induced interaction graph, for instance, the number of connected components, statistics on the degree distribution, and other graph-based properties.We present detailed experimental results measuring the correlation of the stock market events with these features, using Twitter as a data source. Our results show that the most correlated features are the number of connected components and the number of nodes of the interaction graph. The correlation is stronger with the traded volume than with the price of the stock. However, by using a simulator we show that even relatively small correlations between price and micro-blogging features can be exploited to drive a stock trading strategy that outperforms other baseline strategies.

/pdf/correlating-financial-time-series-with-micro-blogging-3yg2vdu5c8.pdf

Correlating financial time series with micro-blogging activity

Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains, including climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences. Spatio-temporal data differ from relational data for which computational approaches are developed in the data-mining community for multiple decades in that both spatial and temporal attributes are available in addition to the actual measurements/attributes. The presence of these attributes introduces additional challenges that needs to be dealt with. Approaches for mining spatio-temporal data have been studied for over a decade in the data-mining community. In this article, we present a broad survey of this relatively young field of spatio-temporal data mining. We discuss different types of spatio-temporal data and the relevant data-mining questions that arise in the context of analyzing each of these datasets. Based on the nature of the data-mining problem studied, we classify literature on spatio-temporal data mining into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining. We discuss the various forms of spatio-temporal data-mining problems in each of these categories.

https://dl.acm.org/doi/pdf/10.1145/3161602

Spatio-Temporal Data Mining: A Survey of Problems and Methods

The increasing volume of streaming data on microblogs has re-introduced the necessity of effective filtering mechanisms for such media. Microblog users are overwhelmed with mostly uninteresting pieces of text in order to access information of value. In this paper, we propose a personalized tweet ranking method, leveraging the use of retweet behavior, to bring more important tweets forward. In addition, we also investigate how to determine the audience of tweets more effectively, by ranking the users based on their likelihood of retweeting the tweets. Finally, conducting a pilot user study, we analyze how retweet likelihood correlates with the interestingness of the tweets.

/pdf/user-oriented-tweet-ranking-a-filtering-approach-to-1x1czamnhk.pdf

User oriented tweet ranking: a filtering approach to microblogs

Deep neural networks (DNNs) have been widely applied to various applications including image classification, text generation, audio recognition, and graph data analysis. However, recent studies have shown that DNNs are vulnerable to adversarial attacks. Though there are several works studying adversarial attack and defense strategies on domains such as images and natural language processing, it is still difficult to directly transfer the learned knowledge to graph structure data due to its representation challenges. Given the importance of graph analysis, an increasing number of works start to analyze the robustness of machine learning models on graph data. Nevertheless, current studies considering adversarial behaviors on graph data usually focus on specific types of attacks with certain assumptions. In addition, each work proposes its own mathematical formulation which makes the comparison among different methods difficult. Therefore, in this paper, we aim to survey existing adversarial learning strategies on graph data and first provide a unified formulation for adversarial learning on graph data which covers most adversarial learning studies on graph. Moreover, we also compare different attacks and defenses on graph data and discuss their corresponding contributions and limitations. In this work, we systemically organize the considered works based on the features of each topic. This survey not only serves as a reference for the research community, but also brings a clear image researchers outside this research domain. Besides, we also create an online resource and keep updating the relevant papers during the last two years. More details of the comparisons of various studies based on this survey are open-sourced at this https URL.

Adversarial Attack and Defense on Graph Data: A Survey.

In this paper, we address the problem of finding authoritative users in a micro-blogging service, Twitter, which is one of the most popular micro-blogging services [1]. Twitter has been gaining a public attention as a new type of information resource, because an enormous number of users transmit diverse information in real time. In particular, authoritative users who frequently submit useful information are considered to play an important role, because useful information is disseminated quickly and widely. To identify authoritative users, it is important to consider actual information flow in Twitter. However, existing approaches only deal with relationships among users. In this paper, we propose TURank (Twitter User Rank), which is an algorithm for evaluating users' authority scores in Twitter based on link analysis. In TURank, users and tweets are represented in a user-tweet graph which models information flow, and ObjectRank is applied to evaluate users' authority scores. Experimental results show that the proposed algorithm outperforms existing algorithms.

TURank: twitter user ranking based on user-tweet graph analysis

Graph convolutional neural networks, which learn aggregations over neighbor nodes, have achieved great performance in node classification tasks. However, recent studies reported that such graph convolutional node classifier can be deceived by adversarial perturbations on graphs. Abusing graph convolutions, a node’s classification result can be influenced by poisoning its neighbors. Given an attributed graph and a node classifier, how can we evaluate robustness against such indirect adversarial attacks? Can we generate strong adversarial perturbations which are effective on not only one-hop neighbors, but more far from the target? In this paper, we demonstrate that the node classifier can be deceived with high-confidence by poisoning just a single node even two-hops or more far from the target. Towards achieving the attack, we propose a new approach which searches smaller perturbations on just a single node far from the target. In our experiments, our proposed method shows 99% attack success rate within two-hops from the target in two datasets. We also demonstrate that $m -$layer graph convolutional neural networks have chance to be deceived by our indirect attack within m-hop neighbors. The proposed attack can be used as a benchmark in future defense attempts to develop graph convolutional neural networks with having adversary robustness.

Indirect Adversarial Attacks via Poisoning Neighbors for Graph Convolutional Networks

How can we release a massive volume of sensitive data while mitigating privacy risks? Privacy-preserving data synthesis enables the data holder to outsource analytical tasks to an untrusted third party The state-of-the-art approach for this problem is to build a generative model under differential privacy, which offers a rigorous privacy guarantee However, the existing method cannot adequately handle high dimensional data In particular, when the input dataset contains a large number of features, the existing techniques require injecting a prohibitive amount of noise to satisfy differential privacy, which results in the outsourced data analysis meaningless To address the above issue, this paper proposes privacy-preserving phased generative model (P3GM), which is a differentially private generative model for releasing such sensitive data P3GM employs the two-phase learning process to make it robust against the noise, and to increase learning efficiency (eg, easy to converge) We give theoretical analyses about the learning complexity and privacy loss in P3GM We further experimentally evaluate our proposed method and demonstrate that P3GM significantly outperforms existing solutions Compared with the state-of-the-art methods, our generated samples look fewer noises and closer to the original data in terms of data diversity Besides, in several data mining tasks with synthesized data, our model outperforms the competitors in terms of accuracy

P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model

Given a collection of seasonal time-series, how can we find regular (cyclic) patterns and outliers (i.e. rare events)? These two types of patterns are hidden and mixed in the time-varying activities. How can we robustly separate regular patterns and outliers, without requiring any prior information? We present CycloneM, a unifying model to capture both cyclic patterns and outliers, and CycloneFact, a novel algorithm which solves the above problem. We also present an automatic mining framework AutoCyclone, based on CycloneM and CycloneFact. Our method has the following properties; (a) effective: it captures important cyclic features such as trend and seasonality, and distinguishes regular patterns and rare events clearly; (b) robust and accurate: it detects the above features and patterns accurately against outliers; (c) fast: CycloneFact takes linear time in the data size and typically converges in a few iterations; (d) parameter free: our modeling framework frees the user from having to provide parameter values. Extensive experiments on 4 real datasets demonstrate the benefits of the proposed model and algorithm, in that the model can capture latent cyclic patterns, trends and rare events, and the algorithm outperforms the existing state-of-the-art approaches. CycloneFact was up to 5 times more accurate and 20 times faster than top competitors.

https://dl.acm.org/doi/pdf/10.1145/3038912.3052595

AutoCyclone: Automatic Mining of Cyclic Online Activities with Robust Tensor Factorization

An objective of the present invention is to ensure anonymity of a movement path and alleviate the degree of abstraction of positioning data included in location information becoming too large when the location information is being tracked in realtime. In order that anonymized information of a first positioning time satisfies a prescribed anonymization index, and that the degree of abstraction of positioning data which is abstracted in the anonymized information falls below a prescribed reference value: an anonymized information group of a second positioning time which is earlier than the first positioning time is segmented into two or more groups; anonymized information is generated which includes the positioning data of the first positioning time, which is abstracted via the segmented groups; and the anonymized information thus generated is associated with the anonymized information of the second positioning time and stored in an anonymized information storage unit.

Tsubasa Takahashi

Papers

TURank: twitter user ranking based on user-tweet graph analysis

Indirect Adversarial Attacks via Poisoning Neighbors for Graph Convolutional Networks

P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model

AutoCyclone: Automatic Mining of Cyclic Online Activities with Robust Tensor Factorization

Information security device and information security method