ABSTRACT To celebrate the 50th Anniversary of the Information Systems Journal, we are delighted to share our research reflections on the article “Community-diversified influence maximization in social networks” published at Information Systems in 2020. Our reflections will highlight the impact of this article on the authors’ research trajectories, its influence on the broader research community, and its contributions to industry practice.

User engagement has recently received significant attention in understanding the decay and expansion of communities in many online social networking platforms. Many user engagement studies have been conducted to find a set of critical (anchored) users in the static social network. However, social networks are highly dynamic and their structures are continuously evolving. In this paper, we target a new research problem called Anchored Vertex Tracking (AVT), aiming to track the anchored users at each timestamp of evolving networks. To address the AVT problem, we develop a greedy algorithm inspired by the previous anchored k-core study in the static networks. Furthermore, we design an incremental algorithm to efficiently solve the AVT problem by utilizing the smoothness of the network structure's evolution. The extensive experiments demonstrate the performance of our proposed algorithms.

Personalized clinical decision support systems are increasingly being adopted due to the emergence of data-driven technologies, with this approach now gaining recognition in critical care. The task of incorporating diverse patient conditions and treatment procedures into critical care decision-making can be challenging due to the heterogeneous nature of medical data. Advances in Artificial Intelligence (AI), particularly Reinforcement Learning (RL) techniques, enables the development of personalized treatment strategies for severe illnesses by using a learning agent to recommend optimal policies. In this study, we propose a Deep Reinforcement Learning (DRL) model with a tailored reward function and an LSTM-GRU-derived state representation to formulate optimal treatment policies for vasopressor administration in stabilizing patient physiological states in critical care settings. Using an ICU dataset and the Medical Information Mart for Intensive Care (MIMIC-III) dataset, we focus on patients with Acute Respiratory Distress Syndrome (ARDS) that has led to Sepsis, to derive optimal policies that can prioritize patient recovery over patient survival. Both the DDQN ( RepDRL-DDQN ) and Dueling DDQN ( RepDRL-DDDQN ) versions of the DRL model surpass the baseline performance, with the proposed model’s learning agent achieving an optimal learning process across our performance measuring schemes. The robust state representation served as the foundation for enhancing the model’s performance, ultimately providing an optimal treatment policy focused on rapid patient recovery.

Influence Maximization(IM), which aims to select a set of users from a social network to maximize the expected number of influenced users, has recently received significant attention for mass communication and commercial marketing. Existing research efforts dedicated to the IM problem depend on a strong assumption: the selected seed users are willing to spread the information after receiving benefits from a company or organization. In reality, however, some seed users may be reluctant to spread the information or need to be paid higher to be motivated. Furthermore, the existing IM works pay little attention to capture users’ influence propagation in the future period. In this paper, we target a new research problem named,ReconnectingTop-$l$lRelationships(RT$l$R) query, which aims to find$l$number of previous existing relationships but being estranged later such that reconnecting these relationships will maximize the expected number of influenced users by the given group in a future period. We prove that the RT$l$R problem is NP-hard. An efficient greedy algorithm is proposed to answer the RT$l$R queries with the influence estimation technique and the well-chosen link prediction method to predict the near future network structure. We also design a pruning method to reduce unnecessary probing from candidate edges. Further, a carefully designed order-based algorithm is proposed to accelerate the RT$l$R queries. Finally, we conduct extensive experiments on real-world datasets to demonstrate the effectiveness and efficiency of our proposed methods.

Machine Unlearning, a pivotal field addressing data privacy in machine learning, necessitates efficient methods for the removal of private or irrelevant data. In this context, significant challenges arise, particularly in maintaining privacy and ensuring model efficiency when managing outdated, private, and irrelevant data. Such data not only compromises model accuracy but also burdens computational efficiency in both learning and unlearning processes. To mitigate these challenges, we introduce a novel framework: Attention-based Machine Unlearning using Federated Reinforcement Learning (FRAMU). This framework incorporates adaptive learning mechanisms, privacy preservation techniques, and optimization strategies, making it a well-rounded solution for handling various data sources, either single-modality or multi-modality, while maintaining accuracy and privacy. FRAMU's strengths include its adaptability in fluctuating data landscapes, its ability to unlearn outdated, private, or irrelevant data, and its support for continual model evolution without compromising privacy. Our experiments, conducted on both single-modality and multi-modality datasets, revealed that FRAMU significantly outperformed baseline models. Additional assessments of convergence behavior and optimization strategies further validate the framework's utility in federated learning applications. Overall, FRAMU advances Machine Unlearning by offering a robust, privacy-preserving solution that optimizes model performance while also addressing key challenges in dynamic data environments.

User engagement has recently received significant attention in understanding the decay and expansion of communities in many online social networking platforms. When a user chooses to leave a social networking platform, it may cause a cascading dropping out among her friends. In many scenarios, it would be a good idea to persuade critical users to stay active in the network and prevent such a cascade because critical users can have significant influence on user engagement of the whole network. Many user engagement studies have been conducted to find a set of critical(anchored)users in the static social network. However, social networks are highly dynamic and their structures are continuously evolving. In order to fully utilize the power of anchored users in evolving networks, existing studies have to mine multiple sets of anchored users at different times, which incurs an expensive computational cost. To better understand user engagement in evolving network, we target a new research problem calledAnchored Vertex Tracking(AVT) in this paper, aiming to track the anchored users at each timestamp of evolving networks. Nonetheless, it is nontrivial to handle the AVT problem which we have proved to be NP-hard. To address the challenge, we develop a greedy algorithm inspired by the previous anchored$k$-core study in the static networks. Furthermore, we design an incremental algorithm to efficiently solve the AVT problem by utilizing the smoothness of the network structure's evolution. The extensive experiments conducted on real and synthetic datasets demonstrate the performance of our proposed algorithms and the effectiveness in solving the AVT problem.

With the boom in cloud computing, data outsourcing in location-based services is proliferating and has attracted increasing interest from research communities and commercial applications. Nevertheless, since the cloud server is probably both untrusted and malicious, concerns about data security and result integrity have become on the rise sharply. In addition, in the single-user situation assumed by most existing works, query users can capture query content from each other even though the queries are encrypted, which may incur the leakage of query privacy. Unfortunately, there exists little work that can commendably assure data security and result integrity in the multi-user setting. To this end, in this article, we study the problem of multi-user, secure, and verifiable$k$nearest neighbor query (MSV$k$kNN). To support MSV$k$NN, we first propose a novel unified structure, called verifiable and secure index (VSI). Based on this, we devise a series of secure protocols to facilitate query processing and develop a compact verification strategy. Given an MSV$k$NN query, our proposed solution can not merely answer the query efficiently while can guarantee: 1) preservingdata privacy,query privacy,result privacy, andaccess patterns privacy; 2) authenticating thecorrectnessandcompletenessof the results; 3) supportingmulti-userwith different keys. Finally, the formal security analysis and complexity analysis are theoretically proven and the performance and feasibility of our proposed approach are empirically evaluated and demonstrated.

With the advent of location-based social networks, users can tag their daily activities in different locations through check-ins. These check-in locations signify user preferences for various socio-spatial activities and can be used to improve the quality of services in some applications such as recommendation systems, advertising, and group formation. To support such applications, in this paper, we formulate a new problem of identifying top-k Socio-Spatial co-engaged Location Selection (SSLS) for users in a social graph, that selects the best set of k locations from a large number of location candidates relating to the user and her friends. The selected locations should be (i) spatially and socially relevant to the user and her friends, and (ii) diversified both spatially and socially to maximize the coverage of friends in the socio-spatial space. To address the NP-hard and challenging problem, we first develop an exact solution by designing some pruning strategies, and also develop an approximate solution by deriving relaxed bounds and advanced termination rules. To accelerate the efficiency, we further develop a fast exact approach and a meta-heuristic approximate approach. Finally, extensive experiments are conducted to evaluate the performance of our proposed algorithms against three adapted existing methods using four real-world datasets.

Influence maximization has recently received significant attention for scheduling online campaigns or advertisements on social network platforms. However, most studies only focus on user influence via cyber interactions while ignoring their physical interactions which are also essential to gauge influence propagation. Additionally, targeted campaigns or advertisements have not received sufficient attention. To address these issues, we first devise a novel holistic influence diffusion model that takes into account both cyber and physical user interactions in an effective and practical way. Based on the new diffusion model, we formulate a new problem ofholistic influence maximization, denoted asHIMquery, for targeted advertisements in a spatial social network. TheHIMquery problem aims to find a minimum set of users whose holistic influence can cover all target users in the network, which belongs to a set covering problem. Since theHIMquery problem is NP-hard, we develop a greedy baseline algorithm and then improve on this algorithm to reduce the computational cost. To deal with large networks, we also design a spatial-social index to maintain the social, spatial and textual information of users, as well as developing an index-based efficient solution. Finally, we conduct extensive experiments using one synthetic and three real-world datasets to validate the efficiency and effectiveness of the proposed holistic influence diffusion model and our developed algorithms.

User engagement has recently received significant attention in understanding decay and expansion of communities in social networks. However, the problem of user engagement hasn't been fully explored in terms of users' specific interests and structural cohesiveness altogether. Therefore, we fill the gap by investigating the problem of community engagement from the perspective of attributed communities. Given a set of keywords W, a structure cohesive parameter k, and a budget parameter l, our objective is to find l number of users who can induce a maximal expanded community. Meanwhile, every community member must contain the given keywords in W and the community should meet the specified structure cohesiveness constraint k. We introduce this problem as best-Anchored Vertex set Exploration (AVE).To solve the AVE problem, we develop a Filter-Verify framework by maintaining the intermediate results using multiway tree, and probe the best anchored users in a best search way. To accelerate the efficiency, we further design a keyword-aware anchored and follower index, and also develop an index-based efficient algorithm. The proposed algorithm can greatly reduce the cost of computing anchored users and their followers. Additionally, we present two bound properties that can guarantee the correctness of our solution. Finally, we demonstrate the efficiency of our proposed algorithms and index. We measure the effectiveness of attributed community-based community engagement model by conducting extensive experiments on five real-world datasets.

The problem of influence maximization has recently received significant attention. However, most studies focused on user influence via cyber interactions while ignoring their physical interactions which are important to gauge influence propagation. Additionally, targeted campaigns or advertisements have not received sufficient attention. To do this, we first devise a novel holistic influence diffusion model and then formulate a new holistic influence maximization query problem and develop three algorithms. Finally, we conduct extensive experiments to evaluate the effectiveness and efficiency of the proposed solutions.