Modeling profile-attribute disclosure in online social networks from a game theoretic perspective

Privacy settings are a crucial part of any online social network as users are confronted with determining which and how many profile attributes to disclose. Revealing more attributes increases users chances of finding friends and yet leaves users more vulnerable to dangers such as identity theft. In this dissertation, we consider the problem of finding the optimal strategy for the disclosure of user attributes in social networks from a game-theoretic perspective. We model the privacy settings\u27 dynamics of social networks with three game-theoretic approaches. In a two-user game, each user selects an ideal number of attributes to disclose to each other according to a utility function. We extend this model with a basic evolutionary game to observe how much of their profiles users are comfortable with revealing, and how this changes over time. We then consider a weighted evolutionary game to investigate the influence of attribute importance, benefit, risk and the network topology on the users\u27 attribute disclosure behavior. The two-user game results show how one user\u27s privacy settings are influenced by the settings of another user. The basic evolutionary game results show that the higher the motivation to reveal attributes, the longer users take to stabilize their privacy settings. Results from the weighted evolutionary game show that: irrespective of risk, users are more likely to reveal their most important attributes than their least important. attributes; when the users\u27 range of influence is increased, the risk factor plays a smaller role in attribute disclosure; the network topology exhibits a considerable effect on the privacy in an environment with risk. Motivation and risk are identified as important factors in determining how efficiently stability of privacy settings is achieved and what settings users will adopt given different parameters. Additionally, the privacy settings are affected by the network topology and the importance users attach to specific attributes. Our models indicate that users of social networks eventually adopt profile settings that provide the highest possible privacy if there is any risk, despite how high the motivation to reveal attributes is. The provided models and the gained results are particularly important to social network designers and providers because they enable us to understand the influence of different factors on users\u27 privacy choices

Fairness in Graph Mining: A Survey

Graph mining algorithms have been playing a significant role in myriad fields over the years. However, despite their promising performance on various graph analytical tasks, most of these algorithms lack fairness considerations. As a consequence, they could lead to discrimination towards certain populations when exploited in human-centered applications. Recently, algorithmic fairness has been extensively studied in graph-based applications. In contrast to algorithmic fairness on independent and identically distributed (i.i.d.) data, fairness in graph mining has exclusive backgrounds, taxonomies, and fulfilling techniques. In this survey, we provide a comprehensive and up-to-date introduction of existing literature under the context of fair graph mining. Specifically, we propose a novel taxonomy of fairness notions on graphs, which sheds light on their connections and differences. We further present an organized summary of existing techniques that promote fairness in graph mining. Finally, we summarize the widely used datasets in this emerging research field and provide insights on current research challenges and open questions, aiming at encouraging cross-breeding ideas and further advances

Federated Few-shot Learning

Federated Learning (FL) enables multiple clients to collaboratively learn a machine learning model without exchanging their own local data. In this way, the server can exploit the computational power of all clients and train the model on a larger set of data samples among all clients. Although such a mechanism is proven to be effective in various fields, existing works generally assume that each client preserves sufficient data for training. In practice, however, certain clients may only contain a limited number of samples (i.e., few-shot samples). For example, the available photo data taken by a specific user with a new mobile device is relatively rare. In this scenario, existing FL efforts typically encounter a significant performance drop on these clients. Therefore, it is urgent to develop a few-shot model that can generalize to clients with limited data under the FL scenario. In this paper, we refer to this novel problem as \emph{federated few-shot learning}. Nevertheless, the problem remains challenging due to two major reasons: the global data variance among clients (i.e., the difference in data distributions among clients) and the local data insufficiency in each client (i.e., the lack of adequate local data for training). To overcome these two challenges, we propose a novel federated few-shot learning framework with two separately updated models and dedicated training strategies to reduce the adverse impact of global data variance and local data insufficiency. Extensive experiments on four prevalent datasets that cover news articles and images validate the effectiveness of our framework compared with the state-of-the-art baselines. Our code is provided\footnote{\href{https://github.com/SongW-SW/F2L}{https://github.com/SongW-SW/F2L}}.Comment: SIGKDD 202

A frequency-domain full waveform inversion method of elastic waves in quantitative defection investigation

857-866Full waveform inversion is a challenging data-fitting procedure based on full wave field modeling to extract quantitative information on elastic properties of subsurface structures. We developed a frequency-domain full-waveform inversion method of elastic waves for stratified media, adopting a quasi-linearization method coupled with a random search algorithm. The inversion process of this method is irrelevant to hypocenter function and can be considered as a kind of combination between the heuristic and non-heuristic inversion methods. To verify our method, we apply it to three numerical two-dimensional models with different intermediate structures (dipping, arched and hollow), and their structures are well revealed. With some pretreatments on response waveforms, such as filtering, normalization and correlation analysis, the full-waveform inversion method is extended to models with damaged area and its feasibility and accuracy verified. Alignment of full waveform inversion method and its cost of computing, several strategies exist to treat this quantitative detecting problem. In Chengdu-Chongqing guest emergency project, the application of full waveform inversion method saves a lot of time. In this method, each section only needs 2 detectors and only need to be hammered twice, while the traditional CT (Computed Tomography) test requires 11 detection filters and at least 11 hammering, and each section has 121 waveform data. In some cases, we can obtain some important priori information through field investigation. The priori information can be used to accelerate the inversion process

Adversarial Attacks on Fairness of Graph Neural Networks

Fairness-aware graph neural networks (GNNs) have gained a surge of attention as they can reduce the bias of predictions on any demographic group (e.g., female) in graph-based applications. Although these methods greatly improve the algorithmic fairness of GNNs, the fairness can be easily corrupted by carefully designed adversarial attacks. In this paper, we investigate the problem of adversarial attacks on fairness of GNNs and propose G-FairAttack, a general framework for attacking various types of fairness-aware GNNs in terms of fairness with an unnoticeable effect on prediction utility. In addition, we propose a fast computation technique to reduce the time complexity of G-FairAttack. The experimental study demonstrates that G-FairAttack successfully corrupts the fairness of different types of GNNs while keeping the attack unnoticeable. Our study on fairness attacks sheds light on potential vulnerabilities in fairness-aware GNNs and guides further research on the robustness of GNNs in terms of fairness. The open-source code is available at https://github.com/zhangbinchi/G-FairAttack.Comment: 32 pages, 5 figure

KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media

Political perspective detection has become an increasingly important task that can help combat echo chambers and political polarization. Previous approaches generally focus on leveraging textual content to identify stances, while they fail to reason with background knowledge or leverage the rich semantic and syntactic textual labels in news articles. In light of these limitations, we propose KCD, a political perspective detection approach to enable multi-hop knowledge reasoning and incorporate textual cues as paragraph-level labels. Specifically, we firstly generate random walks on external knowledge graphs and infuse them with news text representations. We then construct a heterogeneous information network to jointly model news content as well as semantic, syntactic and entity cues in news articles. Finally, we adopt relational graph neural networks for graph-level representation learning and conduct political perspective detection. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods on two benchmark datasets. We further examine the effect of knowledge walks and textual cues and how they contribute to our approach's data efficiency.Comment: accepted at NAACL 2022 main conferenc

Collaborative Graph Neural Networks for Attributed Network Embedding

Graph neural networks (GNNs) have shown prominent performance on attributed network embedding. However, existing efforts mainly focus on exploiting network structures, while the exploitation of node attributes is rather limited as they only serve as node features at the initial layer. This simple strategy impedes the potential of node attributes in augmenting node connections, leading to limited receptive field for inactive nodes with few or even no neighbors. Furthermore, the training objectives (i.e., reconstructing network structures) of most GNNs also do not include node attributes, although studies have shown that reconstructing node attributes is beneficial. Thus, it is encouraging to deeply involve node attributes in the key components of GNNs, including graph convolution operations and training objectives. However, this is a nontrivial task since an appropriate way of integration is required to maintain the merits of GNNs. To bridge the gap, in this paper, we propose COllaborative graph Neural Networks--CONN, a tailored GNN architecture for attribute network embedding. It improves model capacity by 1) selectively diffusing messages from neighboring nodes and involved attribute categories, and 2) jointly reconstructing node-to-node and node-to-attribute-category interactions via cross-correlation. Experiments on real-world networks demonstrate that CONN excels state-of-the-art embedding algorithms with a great margin

Self-Supervised Learning for Recommender Systems: A Survey

In recent years, neural architecture-based recommender systems have achieved tremendous success, but they still fall short of expectation when dealing with highly sparse data. Self-supervised learning (SSL), as an emerging technique for learning from unlabeled data, has attracted considerable attention as a potential solution to this issue. This survey paper presents a systematic and timely review of research efforts on self-supervised recommendation (SSR). Specifically, we propose an exclusive definition of SSR, on top of which we develop a comprehensive taxonomy to divide existing SSR methods into four categories: contrastive, generative, predictive, and hybrid. For each category, we elucidate its concept and formulation, the involved methods, as well as its pros and cons. Furthermore, to facilitate empirical comparison, we release an open-source library SELFRec (https://github.com/Coder-Yu/SELFRec), which incorporates a wide range of SSR models and benchmark datasets. Through rigorous experiments using this library, we derive and report some significant findings regarding the selection of self-supervised signals for enhancing recommendation. Finally, we shed light on the limitations in the current research and outline the future research directions.Comment: 20 pages. Accepted by TKD

Response of the root morphological structure of Fokienia hodginsii seedlings to competition from neighboring plants in a heterogeneous nutrient environment

IntroductionCritical changes often occur in Fokienia hodginsii seedlings during the process of growth owing to differences in the surrounding environment. The most common differences are heterogeneous nutrient environments and competition from neighboring plants.MethodsIn this study, we selected one-year-old, high-quality Fokienia hodginsii seedlings as experimental materials. Three planting patterns were established to simulate different competitive treatments, and seedlings were also exposed to three heterogeneous nutrient environments and a homogeneous nutrient environment (control) to determine their effect on the root morphology and structure of F. hodginsii seedlings.ResultsHeterogeneous nutrient environments, compared with a homogeneous environment, significantly increased the dry matter accumulation and root morphology indexes of the root system of F. hodginsii, which proliferated in nutrient-rich patches, and the P heterogeneous environment had the most significant enhancement effect, with dry matter accumulation 70.2%, 7.0%, and 27.0% higher than that in homogeneous and N and K heterogeneous environments, respectively. Homogeneous environments significantly increased the specific root length and root area of the root system; the dry matter mass and morphological structure of the root system of F. hodginsii with a heterospecific neighbor were higher than those under conspecific neighbor and single-plant treatments, and the root area of the root system under the conspecific neighbor treatment was higher than that under the heterospecific neighbor treatment, by 20% and 23%, respectively. Moreover, the root system under heterospecific neighbor treatment had high sensitivity; the heterogeneous nutrient environment increased the mean diameter of the fine roots of the seedlings of F. hodginsii and the diameter of the vascular bundle, and the effect was most significant in the P heterogeneous environment, exceeding that in the N and K heterogeneous environments. The effect was most significant in the P heterogeneous environment, which increased fine root diameter by 20.5% and 10.3%, respectively, compared with the homogeneous environment; in contrast, the fine root vascular ratio was highest in the homogeneous environment, and most of the indicators of the fine root anatomical structure in the nutrient-rich patches were of greater values than those in the nutrient-poor patches in the different heterogeneous environments; competition promoted most of the indicators of the fine root anatomical structure of F. hodginsii seedlings. According a principal component analysis (PCA), the N, Pm and K heterogeneous environments with heterospecific neighbors and the P heterogeneous environment with a conspecific neighbor had higher evaluation in the calculation of eigenvalues of the PCA.DiscussionThe root dry matter accumulation, root morphology, and anatomical structure of F. hodginsii seedlings in the heterogeneous nutrient environment were more developed than those in the homogeneous nutrient environment. The effect of the P heterogeneous environment was the most significant. The heterospecific neighbor treatment was more conducive to the expansion and development of root morphology of F. hodginsii seedlings than were the conspecific neighbor and single-plant treatments