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With the maturity and popularity of Internet of Things (IoT), the notion of Social Internet of Things (SIoT) has been proposed to support novel applications and networking services for the IoT in more effective and efficient ways. In this paper, we propose a new problem family, namely, Task-Optimized SIoT Selection (TOSS), to find the best group of IoT objects for a given set of tasks in the task pool. TOSS aims to select the target SIoT group such that the target SIoT group is able to easily communicate with each other while maximizing the accuracy of performing the given tasks. We propose two problem formulations, named Bounded Communication-loss TOSS (BC-TOSS) and Robustness Guaranteed TOSS (RG-TOSS), for different scenarios and prove that they are both NP-hard and inapproximable. We propose a polynomial-time algorithm with a performance guarantee for BC-TOSS, and an efficient polynomial-time algorithm to obtain good solutions for RG-TOSS. Moreover, as RG-TOSS is NP-hard and inapproximable within any factor, we further propose Structure-Aware Reinforcement Learning (SARL) to leverage the Graph Convolutional Networks (GCN) and Deep Reinforcement Learning (DRL) to effectively solve RG-TOSS. Experimental results indicate that our proposed algorithms outperform the other deterministic and learning-based baseline approaches.

Hashtag is an important advertising tool and a musthave feature for social media nowadays. In the past, many hashtag recommendation methods have been proposed from different perspectives of images, texts, and users. However, most previous works consider neither the mutual influence between multi-modalities, nor the visual similarity between images. In this paper, we devise a novel model, named Triplet-Attention Graph Network (TAGNet). The rationale behind our method is that visually similar images share some common hashtags. Therefore, we build an image graph, and apply a new Aggregated Graph Convolution (AGC) module to propagate information in a collective way. Furthermore, it is noted that text and user also have rich content information within posts, and we hence propose a Triplet Attention (TA) module to incorporate multi-modalities into node features. Experiments on the large-scale dataset collected from Instagram show that TAGNet achieved significant improvement in recall rate over the best state-of-the-art method.

The social presence theory in social psychology suggests that computer-mediated online interactions are inferior to face-to-face, in-person interactions. In this paper, we consider the scenarios of organizing in person friend-making social activities via online social networks (OSNs) and formulate a new research problem, namely, Hop-bounded Maximum Group Friending (HMGF), that takes into consideration both existing friendships and the likelihood of new friend making in organization of the targeted in person friend-making social activities. To find a set of attendees for such social activities, HMGF is unique and challenging due to the interplay of the group size, the constraint on existing friendships, and the objective of maximizing the likelihood of friend making. We propose an error-bounded approximation algorithm, MaxGF, to efficiently obtain the solutions very close to the optimal solutions. To boost the performance, we devise two graph-theoretical pruning strategies, namely Neighbor Pruning and Core Pruning, which can effectively avoid redundant graph explorations to improve the performance of HMGF. We conduct a user study to validate our problem formulation and perform extensive experiments on real datasets to demonstrate the efficiency and effectiveness of our proposed algorithm. The experimental results manifest that our proposed algorithms outperform the baselines, including the ILP formulation.