Recently, many machine learning-based approaches that effectively solve graph optimization problems have been proposed. These approaches are usually trained on graphs randomly generated with graph generators or sampled from existing datasets. However, we observe that such training graphs lead to poor testing performance if the testing graphs are not generated analogously, i.e., the generalibility of the models trained on those randomly generated training graphs are very limited. To address this critical issue, in this paper, we propose a new framework, named Learning with Iterative Graph Diversification (LIGD), and formulate a new research problem, named Diverse Graph Modification Problem (DGMP), that iteratively generate diversified training graphs and train the models that solve graph optimization problems to significantly improve their performance. We propose three approaches to solve DGMP by considering both the performance of the machine-learning approaches and the structural properties of the training graphs. Experimental results on well-known problems show that our proposed approaches significantly boost the performance of both supervised and reinforcement learning approaches and produce near-optimal results, significantly outperforming the baseline approaches, such as graph augmentation and deep learning-based graph generation approaches.

Enhancing Machine Learning Approaches for Graph Optimization Problems with Diversifying Graph Augmentation

Finding a set of suitable experts with minimized communication overhead to perform a complex task finds a wide spectrum of applications in industry, education, and other scenarios. This class of problems, widely formulated as forming a team of experts in social networks (i.e., team formation problem), is very challenging due to its NP-hardness and has attracted much research attention. Although various effective and elegant algorithms have been proposed to address this important problem, the methods are usually manually designed and handcrafted, which require considerable human efforts. In this article, we make our first attempt to automate the algorithm design with a machine learning-based approach, named reinforcement learning-based expert team identification (RELEXT). Moreover, we also propose two novel graph embedding methods to consider two important dimensions of the team formation problem, i.e., the skill and social dimensions. We evaluate the proposed approaches on multiple large-scale real datasets. The experimental results show that our proposed approaches outperform the other baselines in terms of solution quality and efficiency. 

Learning to Extract Expert Teams in Social Networks

Extracting dense structures in a social network is a fundamental task in graph mining and can find many real-world applications The temporal social network augments the conventional social network with the temporal dimension, and extracting dense structures enables us to understand the period of time for which the dense structures exist In this paper, we propose the new notion of (L, K)-lasting core, which is a densely connected subgraph lasting for a sufficiently long period of time in the temporal social network We propose a polynomial-time algorithm to obtain the maximum (L, K)-lasting core with various processing strategies to boost the efficiency We conduct extensive experiments on multiple datasets to validate the effectiveness and efficiency of the proposed approach The experimental results show that our proposed approaches outperform the other baseline approaches in terms of solution quality and efficiency

Maximum (L, K)-Lasting Cores in Temporal Social Networks


 Research on new queries for finding dense subgraphs and groups has been actively pursued due to their many applications, especially in social network analysis and graph mining. However, existing work faces two major weaknesses: i) incapability of supporting personalized neighborhood density, and ii) inability to find sparse groups. To tackle the above issues, we propose a new query, called Density-Customized Social Group Query (DCSGQ), that accommodates the need for
 personalized density
 by allowing individual users to flexibly configure their social tightness (and sparseness) for the target group. The proposed DCSGQ is general due to flexible in configuration of neighboring social density in queries. We prove the NP-hardness and inapproximability of DCSGQ, formulate an Integer Program (IP) as a baseline, and propose an efficient algorithm, FSGSel-RR, by relaxing the IP. We then propose a fixed-parameter tractable algorithm with a performance guarantee, named FSGSel-TD, and further combine it with FSGSel-RR into a hybrid approach, named FSGSel-Hybrid, in order to strike a good balance between solution quality and efficiency. Extensive experiments on multiple large real datasets demonstrate the superior solution quality and efficiency of our approaches over existing subgraph and group queries.


Density Personalized Group Query

Studies in Management and Sociology indicate that the diversity of job-related attributes of group members (group diversity) is crucial for boosting the group performance, because the diversity brings different knowledge and enables more innovative ideas and solutions. In addition, the social tightness of the group members is also important for effective communication, which is also an important factor for group performance. In this paper, we propose a new research problem, named Social-aware Diversity-optimized Group Extraction (SDGE), which considers the above two important factors jointly to extract a socially tight group with optimized group diversity from the social network. We formally formulate the SDGE problem and propose algorithm Group Shrinking for Diversity Maximization (GSDM), which is a 3-approximation algorithm for SDGE. We prove the performance guarantee of GSDM and conduct extensive experiments on real datasets to evaluate the performance of GSDM. The results indicate that GSDM outperforms the other baselines in both solution quality and efficiency.

On Extracting Social-Aware Diversity-Optimized Groups in Social Networks

Three essential criteria are important for social activity planning: (1) finding attendees familiar with the initiator, (2) ensuring most attendees have tight social relations with each other, and (3) selecting an activity period available to all. In this paper, we propose the Social-Temporal Group Query (STGQ) to find suitable time and attendees with minimum total social distance. We first prove that the problem is NP-hard and inapproximable within any ratio. Next, we design two algorithms, SGSelect and STGSelect , which include effective pruning techniques to substantially reduce running time. Moreover, as users may iteratively adjust query parameters to fine tune the results, we study the problem of Subsequent Social Group Query (SSGQ) . We propose the Accumulative Search Tree and Social Boundary , to cache and index intermediate results of previous queries in order to accelerate subsequent query processing. Experimental results indicate that SGSelect and STGSelect are significantly more efficient than baseline approaches. With the caching mechanisms, processing time of subsequent queries can be further reduced by 50-75 percent. We conduct a user study to compare the proposed approach with manual activity coordination. The results show that our approach obtains higher quality solutions with lower coordination effort, thereby increasing the users’ willingness to organize activities.

On Efficient Processing of Group and Subsequent Queries for Social Activity Planning

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On Efficient Processing of Group and Subsequent Queries for Social Activity Planning