On optimal preference diffusion over social networks

It was well observed that a user’s preference over a product changes based on his/her friends’ preferences, and this phenomenon is called “preference diffusion”, and several models have been proposed for modeling the preference diffusion process. These models share an idea that the diffusion process involves many iterations, and in each iteration, each user has his/her preference affected by some other preferences (e.g., those of his/her friends). When computing users’ preferences after a certain number of iterations, these models use users’ preferences at the end of that iteration only, which we believe is not desirable since users’ preferences at the end of other iterations should also have some effects on users’ final preferences. Therefore, in this paper, we propose a new model for preference diffusion, which takes into consideration users’ preferences at each iteration for computing users’ final preferences. Under the new model, we study two problems for optimizing the preference diffusion process with respect to two different objectives. One is easy to solve for which we design an exact algorithm and the other is NP-hard for which we design a $(1-1∕e)$-factor approximate algorithm. We conducted extensive experiments on real datasets which verified our proposed model and algorithms.     

基于用户潜在时效偏好的推荐算法*

针对推荐系统中用户兴趣的潜在性以及高时效性业务场景下用户兴趣的不稳定性和时间迁移性进行研究,提出一种基于用户潜在时效偏好的推荐方法。通过深入分析用户的历史行为与用户潜在兴趣的关系,提出基于概率主题模型的用户兴趣挖掘方法,避免了传统推荐方式对用户兴趣潜在性的忽略;同时,基于高时效业务对时间敏感性的考虑,结合隐马尔科夫模型对用户兴趣进行实时捕获,发现用户的兴趣迁移序列,并以此提出基于用户时效偏好的推荐方法。最后通过相关实验验证了所提出方法的可行性。     

Inferring preference correlations from social networks

Identifying consumer preferences is a key challenge in customizing electronic commerce sites to individual users. The increasing availability of online social networks provides one approach to this problem: people linked in these networks often share preferences, allowing inference of interest in products based on knowledge of a consumer’s network neighbors and their interests. This paper evaluates the benefits of inference from online social networks in two contexts: a random graph model and a web site allowing people to both express preferences and form distinct social and preference links. We determine conditions on network topology and preference correlations leading to extended clusters of people with similar interests. Knowledge of when such clusters occur improves the usefulness of social network-based inference for identifying products likely to interest consumers based on information from a few people in the network. Such estimates could help sellers design customized bundles of products and improve combinatorial auctions for complementary products.     

Tuning user profiles based on analyzing dynamic preference in document retrieval systems

Modeling users’ preferences and needs is one of the most important personalization tasks in information retrieval domain. In this paper a model for user profile tuning in document retrieval systems is considered. Methods for tuning the user profile based on analysis of user preferences dynamics are experimentally evaluated to check whether with growing history of user activity the created user profile can converge to his preferences. As the statistical analysis of series of simulations has shown, proposed methods of user profile actualization are effective in the sense that the distance between user preferences and his profile becomes smaller and smaller along with time.     

Matching user identities across social networks with limited profile data

Privacy preservation is a primary concern in social networkswhich employ a variety of privacy preservations mechanisms to preserve and protect sensitive user information including age, location, education, interests, and others. The task of matching user identities across different social networks is considered a challenging task. In this work, we propose an algorithm to reveal user identities as a set of linked accounts from different social networks using limited user profile data, i.e., user-name and friendship. Thus, we propose a framework, ExpandUIL, that includes three standalone algorithms based on (i) the percolation graph matching in ExpandFullName algorithm, (ii) a supervised machine learning algorithm that works with the graph embedding, and (iii) a combination of the two, ExpandUserLinkage algorithm. The proposed framework as a set of algorithms is significant as, (i) it is based on the network topology and requires only name feature of the nodes, (ii) it requires a considerably low initial seed, as low as one initial seed suffices, (iii) it is iterative and scalable with applicability to online incoming stream graphs, and (iv) it has an experimental proof of stability over a real ground-truth dataset. Experiments on real datasets, Instagram and VK social networks, show upto 75% recall for linked accounts with 96% accuracy using only one given seed pair.     

社会网络的隐私保护研究综述*

某些网站将匿名处理后的社会网络数据公开发布,或者提供给科研机构、大学或者其他组织和个人使用,而这些数据往往侵犯了用户的隐私,但有关社会网络中个人信息安全和隐私保护的研究却处于起步阶段。综述了当前在线社会网络的研究成果,主要就社会网络及其隐私漏洞、信息泄露、再识别攻击、聚集攻击、推理攻击等进行了分析,并对今后的发展提出了预测,为社会网络的科研指出了可行的研究方向。     

Learning disentangled user representation with multi-view information fusion on social networks

User representation learning is one prominent and critical task of user analysis on social networks, which derives conceptual user representations to improve the inference of user intentions and behaviors. Previous efforts have shown its substantial value in multifarious real-world applications, including product recommendation, textual content modeling, link prediction, and many more. However, existing studies either underutilize multi-view information, or neglect the stringent entanglement among underlying factors that govern user intentions, thus deriving deteriorated representations. To overcome these shortages, this paper proposes an adversarial fusion framework to fully exploit substantial multi-view information for user representation, consisting of a generator and a discriminator. The generator learns representations with a variational autoencoder, and is forced by the adversarial fusion framework to pay specific attention to substantial informative signs, thus integrating multi-view information. Furthermore, the variational autoencoder used in the generator is novelly designed to capture and disentangle the latent factors behind user intentions. By fully utilizing multi-view information and achieving disentanglement, our model learns robust and interpretable user representations. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of our proposed model.     

基于用户兴趣的跨网络用户身份识别算法

针对现有算法对用户兴趣在跨网络用户身份识别中作用的忽视以及时间复杂度高的问题,提出了基于用户兴趣的跨社交网络用户身份识别算法（UI-UI）。首先利用分块思想对用户节点进行初筛选,以提升算法效率、降低时间复杂度;其次,根据用户产生内容（UGC）和用户社交关系对用户兴趣进行建模,并计算兴趣相似度作为身份识别的依据;最后利用半监督学习的方法进行跨网络用户身份识别。通过在真实社交网络中进行实验,结果表明UI-UI算法能有效识别跨网络用户,且准确率和召回率稳定,运行时间显著减少。     

A novel approach for analyzing dynamics in neural networks with saturated characteristics

Current theories about the dynamics of neural networks with nonlinear characteristics and parameterized by set of parameters are mostly based on approximations in one way or another. In this paper we first introduce a rigorous approach which allows us to check in which parameter region a given saturated state is an attractor of the dynamics: a saturated state w=(w _i , i=1,...,N){-1,1} ^N is an attractor of the dynamics if and only if there is a local field gap between neurons in J ⁺ (w)={i, w _i =1} and J ^- (w)={i, w _i =–1}. Then we apply the result to analyze several models in neural networks. In particular in the Hopfield model we calculate the capacity and give an exact relation between the capacity and the threshold.     

社交网络下考虑决策者角色差异的舆情演化模型

互联网加速了舆情的发展,舆情讨论时刻都在发生.为了探索决策者角色差异对舆情演化的影响,改进传统DeGroot模型,在社交网络环境下考虑多种决策者角色共存的舆情演化模型.将决策者分为权威者和非权威者,从社会心理学的角度将非权威者随机分为服从者、反抗者和独立者.针对传统模型的权重确定方式较为机械的不足,引入节点在社会网络中的特征向量中心性,提出新的权重确定方法.每一步舆情演化均分为两个阶段:第1阶段所有决策者都进行舆情演化,随后服从者和反抗者在第2阶段对其第1阶段的观点进行调整.在连通的Erdddotos-Rényi网络中进行仿真实验,结果显示,服从者总是会与权威者达成共识,且该共识由权威者的意见决定;反抗者聚集在与服从者和权威者的共识较远的意见簇;独立者的稳定意见则散落分布在二者之间,且更加靠近服从者和权威者的共识.     

A general framework for analyzing techno-behavioural dynamics on networks

A general framework for assessing future impacts of technology on society and environment is presented. The dynamics between human activity and technological systems impact upon many processes in society and nature. This involves non-linear dynamics requiring an understanding of how technology and human behaviour influence each other and co-evolve. Conventionally, technological and behavioural systems are analyzed as separate entities. We develop an integrated theoretical and methodological approach termed techno-behavioural dynamics focussing on networked interactions between technology and behaviour across multiple system states. We find that positive feedback between technology learning, evolving preferences and network effects can lead to tipping points in complex sociotechnical systems. We also demonstrate how mean-field and agent-based models are complimentary for capturing a hierarchy of analytical resolutions in a common problem domain. Assessing and predicting co-evolutionary dynamics between technology and human behaviour can help avoid systems lock-in and inform a range of adaptive responses to environmental and societal risk.     

基于用户关系的跨社交网络用户身份关联方法

为识别出不同社交网络平台中属于同一自然人的账号,提出了一种基于用户关系的跨社交网络用户身份关联方法。首先,设计了基于网络表示学习的用户关系提取模块,将大规模用户关系转换至低维向量空间进行表示;然后,针对异构信息网络改进了传统网络表示学习算法,提出了CSN_LINE算法,实现融合跨社交网络先验关联关系的网络表示;最后,构建了基于多层感知机的用户身份关联模型。实验结果表示,提出的方法与目前先进的方法相比,综合指标F1值和正确率的提高均超过12%,证明了该方法的合理性和有效性。     

Inferring user interests in microblogging social networks: a survey

With the growing popularity of microblogging services such as Twitter in recent years, an increasing number of users are using these services in their daily lives. The huge volume of information generated by users raises new opportunities in various applications and areas. Inferring user interests plays a significant role in providing personalized recommendations on microblogging services, and also on third-party applications providing social logins via these services, especially in cold-start situations. In this survey, we review user modeling strategies with respect to inferring user interests from previous studies. To this end, we focus on four dimensions of inferring user interest profiles: (1) data collection, (2) representation of user interest profiles, (3) construction and enhancement of user interest profiles, and (4) the evaluation of the constructed profiles. Through this survey, we aim to provide an overview of state-of-the-art user modeling strategies for inferring user interest profiles on microblogging social networks with respect to the four dimensions. For each dimension, we review and summarize previous studies based on specified criteria. Finally, we discuss some challenges and opportunities for future work in this research domain.     

Community detection in social networks using user frequent pattern mining

Recently, social networking sites are offering a rich resource of heterogeneous data. The analysis of such data can lead to the discovery of unknown information and relations in these networks. The detection of communities including ‘similar’ nodes is a challenging topic in the analysis of social network data, and it has been widely studied in the social networking community in the context of underlying graph structure. Online social networks, in addition to having graph structures, include effective user information within networks. Using this information leads to enhance quality of community discovery. In this study, a method of community discovery is provided. Besides communication among nodes to improve the quality of the discovered communities, content information is used as well. This is a new approach based on frequent patterns and the actions of users on networks, particularly social networking sites where users carry out their preferred activities. The main contributions of proposed method are twofold: First, based on the interests and activities of users on networks, some small communities of similar users are discovered, and then by using social relations, the discovered communities are extended. The F-measure is used to evaluate the results of two real-world datasets (Blogcatalog and Flickr), demonstrating that the proposed method principals to improve the community detection quality.     

社交网络用户影响力分析ABP算法研究与应用

社交网络作为一种交往方式,已经深入人心。其用户数据在这个大数据时代蕴藏着大量的价值。随着Twitter API的开放,社交网络Twitter俨然成为一个深受欢迎的研究对象,而用户影响力更是其中的研究热点。PageRank算法计算用户影响力已经由来已久,但是它太依赖于用户之间的关注关系,排名不具备时效性。引入用户活跃度的改进PageRank算法,具备一定的时效性,但是不具有足够的说服力和准确性。研究了一种新的基于时间分布用户活跃度的ABP算法,并为不同时段的活跃度加以相应的时效权重因子。最后,以Twitter为研究对象,结合社交关系网,通过实例分析说明ABP算法更具时效性和说服力,可以比较准确地提高活跃用户的排名,降低非活跃用户排名。     

Geotag propagation in social networks based on user trust model

In the past few years sharing photos within social networks has become very popular. In order to make these huge collections easier to explore, images are usually tagged with representative keywords such as persons, events, objects, and locations. In order to speed up the time consuming tag annotation process, tags can be propagated based on the similarity between image content and context. In this paper, we present a system for efficient geotag propagation based on a combination of object duplicate detection and user trust modeling. The geotags are propagated by training a graph based object model for each of the landmarks on a small tagged image set and finding its duplicates within a large untagged image set. Based on the established correspondences between these two image sets and the reliability of the user, tags are propagated from the tagged to the untagged images. The user trust modeling reduces the risk of propagating wrong tags caused by spamming or faulty annotation. The effectiveness of the proposed method is demonstrated through a set of experiments on an image database containing various landmarks.     

Integrating data from user activities of social networks into public administrations

Linking social networks with government applications promises various benefits, such as improving citizens’ public engagement, increasing transparency and openness in government actions, and new or enhanced government services. The research goal is to drive innovation in governments through the integration of user activities from social networks into government applications. Instead of using third-party social media tools, we call for self-developing integration software, so that the government retains full control of the sensitive government data that is linked to social network user data. Following a design science approach, we developed a data model of user activities in social networks. Our 40 user activity types conceptualize the common fundamental data structure and are a means for comparing current features of ten prominent social networks. We find that a substantial share of user activities can be mutually integrated by wrapping social network Application Programming Interfaces (APIs).     

On point-duration networks for temporal reasoning

We present here a point-duration network formalism which extends the point algebra model to include additional variables that represent durations between points of time. Thereafter the new qualitative model is enlarged for allowing unary metric constraints on points and durations, subsuming in this way several point-based approaches to temporal reasoning. We deal with some reasoning tasks within the new models and we show that the main problem, deciding consistency, is NP-complete. However, tractable special cases are identified and we show efficient algorithms for checking consistency, finding a solution and obtaining the minimal network.     

On packing arborescences in temporal networks

A temporal network is a directed graph in which each arc has a time label specifying the time at which its end vertices communicate. An arborescence in a temporal network is said to be time-respecting, if the time labels on every directed path from the root in this arborescence are monotonically non-decreasing. In this paper, we consider a characterization of the existence of arc-disjoint time-respecting arborescences in temporal networks.