Influence Maximization (IM) is a crucial problem in social network analysis and has been extensively studied. Traditional approaches rely on designing approximation algorithms using network sampling; however, these methods lack generalizability and depend on an explicit definition of the influence diffusion model as input. Recently, researchers have turned to deep learning methods to address the shortcomings of traditional IM algorithms, but current learning-based IM algorithms still suffer from severe deficiencies in scalability and generalizability. In this paper, we propose IMGNN, a simple, efficient, effective, and generalizable algorithm powered by graph neural networks. IMGNN is a learning-based IM algorithm with strong generalization capability that reduces the overhead of model retraining while also providing fast seed set inference speed. As a result, IMGNN achieves better performance in terms of both efficiency and effectiveness compared to existing IM algorithms. Its exceptional generalization capability also enables it to be trained on small-scale graphs and directly infer the seed node set for large-scale graphs. IMGNN achieves these advantages by adopting a novel design for feature construction and model training, utilizing features constructed from influence propagations over graphs with randomly skipped nodes. This approach enables IMGNN to avoid overfitting to specific network structures while employing a unique technique to improve time efficiency by training on smaller networks. We have conducted extensive experiments using real-world social networks with up to 40 million nodes, and the results strongly demonstrate the superiority of IMGNN in terms of influence spread, seed node set inference speed, and generalizability.

Incomplete node features are ubiquitous in real-world scenarios such as user profiling and cold-start recommendation, which severely hinders the practical deployment of graph learning systems (e.g., GNNs). Existing solutions typically rely on diffusion-based structural smoothing (e.g., feature propagation) to impute missing values. However, we find that these approaches suffer from structural overfitting, leading to three progressive challenges: 1) performance degradation on disjoint graphs, 2) loss of semantic diversity due to over-smoothing, and 3) feature distribution shift when generalizing to unseen graph structures (inductive tasks). To address these challenges, we introduce the DART framework. It begins by employing Global Structural Augmentation (GSA), which establishes global correlations to bridge disjoint components and extend diffusion coverage. Building upon this, we design a semantic rectifier based on masked autoencoding. This module learns the latent feature manifold to recover natural semantic details. Crucially, we introduce a test-time distribution rectification mechanism that projects structurally biased features back onto the learned manifold during inference, effectively bridging the inductive distribution gap. Furthermore, considering that synthetic masking fails to reflect realworld sparsity, we present a new dataset Sailing collected from voyage records with naturally missing attributes. Extensive experiments on six public datasets and Sailing demonstrate that DART significantly outperforms state-of-the-art methods in both transductive and inductive settings. Our code and dataset are available at https://github.com/yfsong00/DART.

Representative subset selection from data streams is a critical problem with wide-ranging applications in web data mining and machine learning, such as social media marketing, big data summarization, and recommendation systems. This problem is often framed as maximizing a monotone submodular function subject to a knapsack constraint, where each data element in the stream has an associated cost, and the goal is to select elements within a budget B to maximize revenue. However, existing algorithms typically rely on restrictive assumptions about the costs of data elements, and their performance bounds heavily depend on the budget B. As a result, these algorithms are only effective in limited scenarios and have super-linear time complexity, making them unsuitable for large-scale data streams. In this paper, we introduce the first linear-time streaming algorithms for this problem, without any assumptions on the data stream, while also minimizing memory usage. Specifically, our single-pass streaming algorithm achieves an approximation ratio of 1/8-ε under O (n) time complexity and O(k log 1/ε) space complexity, where k is the largest cardinality of any feasible solution. Our multi-pass streaming algorithm improves this to a (1/2-ε)-approximation using only three passes over the stream, with O (n/ε log 1/ε) time complexity and O(k/ε log 1/ε) space complexity. Extensive experiments across various applications related to web data mining and social media marketing demonstrate the superiority of our algorithms in terms of both effectiveness and efficiency.

Submodular optimization has been identified as a powerful tool for many data mining applications, where a representative subset of moderate size needs to be extracted from a large-scale dataset. In scenarios where data points possess sensitive attributes such as age, gender, or race, it becomes imperative to integrate fairness measures into submodular optimization to mitigate bias and discrimination. In this paper, we study the fundamental problem of fair submodular maximization subject to a knapsack constraint and propose the first streaming algorithm for it with provable performance guarantees for both monotone and non-monotone submodular functions. As a byproduct, we also propose a streaming algorithm for submodular maximization subject to a partition matroid and a knapsack constraint, significantly improving the performance bounds achieved by previous work. We conduct extensive experiments on real-world applications such as movie recommendation, image summarization, and maximum coverage in social networks. The experimental results strongly demonstrate the superiority of our proposed algorithms in terms of both fairness and utility.

Personalized Session-based recommendation (PSBR) is a general and challenging task in the real world, aiming to recommend a session’s next clicked item based on the session’s item transition information and the corresponding user’s historical sessions. A session is defined as a sequence of interacted items during a short period. The PSBR problem has a natural hierarchical architecture in which each session consists of a series of items, and each user owns a series of sessions. However, the existing PSBR methods can merely capture the pairwise relation information within items and users. To effectively capture the hierarchical information, we propose a novel hierarchical hypergraph neural network to model the hierarchical architecture. Moreover, considering that the items in sessions are sequentially ordered, while the hypergraph can only model the set relation, we propose a directed graph aggregator (DGA) to aggregate the sequential information from the directed global item graph. By attentively combining the embeddings of the above two modules, we propose a framework dubbed H3GNN (Hybrid Hierarchical HyperGraph Neural Network). Extensive experiments on three benchmark datasets demonstrate the superiority of our proposed model compared to the state-of-the-art methods, and ablation experiment results validate the effectiveness of all the proposed components.

The problem of constrained subset selection from a large data stream for profit maximization has many applications in web data mining and machine learning, such as social advertising, team formation and recommendation systems. Such a problem can be formulated as maximizing a regularized submodular function under certain constraints. In this paper, we consider a generalized k-system constraint, which captures various requirements in real-world applications. For this problem, we propose the first streaming algorithm with provable performance bounds, leveraging a novel multitudinous distorted filter framework. The empirical performance of our algorithm is extensively evaluated in several applications including web data mining and recommendation systems, and the experimental results demonstrate the superiorities of our algorithm in terms of both effectiveness and efficiency.

A lot of applications in web economics need to maximize the revenue under a budget for payments and also guarantee the truthfulness of users, so Budget-Feasible Mechanism (BFM) Design has aroused great interests during last decade. Most of the existing BFMs concentrate on maximizing a monotone submodular function subject to a knapsack constraint, which is insufficient for many applications with complex objectives or constraints. Observing this, the recent studies (e.g., [4, 5, 11]) have considered non-monotone submodular objectives or more complex constraints such as a k-system constraint. In this study, we follow this line of research and propose truthful BFMs with improved performance bounds for non-monotone submodular objectives with or without a k-system constraint. Our BFMs leverage the idea of providing random prices to users while deferring the decision on the final winning set, and are also based on a novel randomized algorithm for the canonical constrained submodular maximization problem achieving better performance bounds compared to the state-of-the-art. Finally, the effectiveness and efficiency of our approach are demonstrated by extensive experiments on several applications about social network marketing, crowdsourcing and personalized recommendation.

Streaming Session-Based Recommendation (SSBR) is a challenging problem as user preferences in sessions are continually drifting with sessions generated chronologically. In recent years, some SSBR models have been proposed to address this problem by reservoir technique and Graph Neural Networks (GNN) which help to preserve a representative sketch of the historical data and extract item transition information in sessions. However, there are two critical problems in existing methods: (1) most existing methods only focus on the local session information without exploiting the information of other sessions and users; (2) GNN models in existing SSBR methods are unable to capture the importance of different user features. To address the problems mentioned above, we propose a novel architecture namedGlobalItem andUser embeddingAssistedGraphNeuralNetwork (GIUA-GNN) for combining the global user and item information in an attentional manner with local session information for the recommendation. We also propose a novel architecture of graph neural network which utilizes the attention mechanism for better extracting the importance of different features of user embeddings namedBi-directedAttentionalGraphConvolutionalNetwork (BA-GCN). Extensive experiments on three different sizes of real-world datasets have been conducted to demonstrate the superiority of our model on metrics MRR and Recall.

Thanks to billions of users in online social networks (OSNs), viral marketing becomes one of the most effective promotion channels for various new products or campaigns. Influence maximization is a classic problem in viral marketing, which has been extensively studied in the past two decades. Existing algorithms for influence maximization, however, mostly focus on single machine processing. To address the influence maximization problem on a massive scale, we design distributed algorithms via a cluster of machines, which can effectively speed up the computation while maintaining the state-of-the-art (1 -1/e-c)-approximation guarantee. Our distributed algorithms consist of two building blocks: (i) distributed reverse influence sampling, and (ii) element-distributed maximum coverage. We carry out extensive experiments on real datasets with millions of nodes and billions of edges to demonstrate the scalability of our distributed algorithms for both influence maximization and maximum coverage. In particular, our distributed algorithms accelerate the state-of-the-art IMM algorithm by 31x-56x times using a machine with 64 cores.

The interactive graph search (IGS) problem aims to locate an initially unknown target node leveraging human intelligence. In IGS, we can gradually find the target node by sequentially asking humans some reachability queries like "is the target node reachable from a given node x?". However, human workers may make mistakes when answering these queries. Motivated by this concern, in this paper, we study a noisy version of the IGS problem. Our objective in this problem is to minimize the query complexity while ensuring accuracy. We propose a method to select the query node such that we can push the search process as much as possible and an online method to infer which node is the target after collecting a new answer. By rigorous theoretical analysis, we show that the query complexity of our approach is near-optimal up to a constant factor. The extensive experiments on two real datasets also demonstrate the superiorities of our approach.

We consider the revenue maximization problem in social advertising, where a social network platform owner needs to select seed users for a group of advertisers, each with a payment budget, such that the total expected revenue that the owner gains from the advertisers by propagating their ads in the network is maximized. Previous studies on this problem show that it is intractable and present approximation algorithms. We revisit this problem from a fresh perspective and develop novel efficient approximation algorithms, both under the setting where an exact influence oracle is assumed and under one where this assumption is relaxed. Our approximation ratios significantly improve upon the previous ones. Furthermore, we empirically show, using extensive experiments on four datasets, that our algorithms considerably outperform the existing methods on both the solution quality and computation efficiency.

We study the min-cost seed selection problem in online social networks for viral marketing, where the goal is to select a set of seed nodes with the minimum total cost such that the expected number of influenced nodes in the network exceeds a predefined threshold. We propose several algorithms that outperform the previous studies both on the theoretical approximation ratio and on the experimental performance. In the case where the nodes have heterogeneous costs, our algorithms are the first bi-criteria approximation algorithms with polynomial running time and provable approximation ratio. In the case where the users have uniform costs, our algorithms achieve logarithmic approximation ratio and provable time complexity which is smaller than that of the existing algorithms in orders of magnitude. We conduct extensive experiments using real social networks. The experimental results show that, our algorithms significantly outperform the existing algorithms both on the total cost and on the running time, and also scale well to billion-scale networks.

We consider the edge uncertainty in an undirected graph and study the k -median (resp. k -center) problems, where the goal is to partition the graph nodes into k clusters such that the average (resp. minimum) connection probability between each node and its cluster's center is maximized. We analyze the hardness of these problems, and propose algorithms that provide considerably improved approximation guarantees than the existing studies do. Specifically, our algorithms offer (1 -- 1/e)-approximations for the k -median problem and (OPTck)-approximations for the k -center problem, where OPTck is the optimal objective function value for k -center. In addition, our algorithms incorporate several non-trivial optimizations that significantly enhance their practical efficiency. Extensive experimental results demonstrate that our algorithms considerably outperform the existing methods on both computation efficiency and the quality of clustering results.

Recently, the proliferation of event-based social services has made it possible for organizing personalized offline events through the users' information shared online. In this paper, we study the budget-constrained influential social event organization problem, where the goal is to select a group of influential users with required features to organize a social event under a budget B. We show that our problem is NP-hard and can be formulated as a submodular maximization problem with mixed packing and covering constraints. We then propose several polynomial time algorithms for our problem with provable approximation ratios, which adopt a novel “surrogate optimization” approach and the method of reverse-reachable set sampling. Moreover, we also consider the case where the influence spread function is unknown and can be arbitrarily selected from a set of candidate submodular functions, and extend our algorithms to address a “robust influential event organization” problem under this case. Finally, we conduct extensive experiments using real social networks to test the performance of our algorithms, and the experimental results demonstrate that our algorithms significantly outperform the prior studies both on the running time and on the influence spread.

Recently, the proliferation of event-based social services has made it possible for organizing personalized offline events through the users' information shared online. In this paper, we study the budget-constrained influential social event organization problem, where the goal is to select a group of influential users with required features to organize a social event under a budget B. We show that our problem is NP-hard and can be formulated as a submodular maximization problem with mixed packing and covering constraints. We then propose several polynomial time algorithms for our problem with provable approximation ratios, which adopt a novel "surrogate optimization approach and the method of reverse-reachable set sampling. Compared with some related work that can only handle special cases of our problem but with exponential time complexity, our algorithms are much more efficient, and their superiorities on both the running time and the influence spread are demonstrated through extensive experiments using real social networks."

Given a social network G , the influence maximization (IM) problem seeks a set S of k seed nodes in G to maximize the expected number of nodes activated via an influence cascade starting from S. Although a lot of algorithms have been proposed for IM, most of them only work under the non-adaptive setting, i.e., when all k seed nodes are selected before we observe how they influence other users. In this paper, we study the adaptive IM problem, where we select the k seed nodes in batches of equal size b , such that the choice of the i -th batch can be made after the influence results of the first i - 1 batches are observed. We propose the first practical algorithms for adaptive IM with an approximation guarantee of 1 − exp(ξ − 1) for b = 1 and 1 − exp(ξ − 1 + 1/ e ) for b > 1, where ξ is any number in (0, 1). Our approach is based on a novel AdaptGreedy framework instantiated by non-adaptive IM algorithms, and its performance can be substantially improved if the non-adaptive IM algorithm has a small expected approximation error. However, no current non-adaptive IM algorithms provide such a desired property. Therefore, we further propose a non-adaptive IM algorithm called EPIC, which not only has the same worst-case performance bounds with that of the state-of-the-art non-adaptive IM algorithms, but also has a reduced expected approximation error. We also provide a theoretical analysis to quantify the performance gain brought by instantiating AdaptGreedy using EPIC, compared with a naive approach using the existing IM algorithms. Finally, we use real social networks to evaluate the performance of our approach through extensive experiments, and the experimental experiments strongly corroborate the superiorities of our approach.