Academic social network analysis has attracted significant attention. For each researcher in such a network, he/she has several research interests. We regard these researchers sharing common interests as a research community. For each community, it may be attractive or not to researchers from other communities. In this paper, we study a new and interesting problem: which is the most attractive research community in the academic social network? Here, attractive research communities are those potentially valuable and increasingly popular communities, which are different from hot communities. To address this problem, we first extract both of the internal and external features of attractive research communities. The internal feature refers to the novelty of the topic in the research community and the external feature refers to the researchers’ transition among the communities. Intuitively, a community with a novel topic attracts the researchers from other research communities can be considered as the attractive community. Based on the extracted features, we design a novel Attractive Research community Ranking (ARTRank) algorithm to rank the research communities. The core idea of this algorithm lies in two measurements for each community: a positiveness score and a negativeness score, which measure the attractiveness of a community from the in-attention aspect and the out-attention aspect, respectively. Similar to HITS, these two scores are calculated in an iterative way until convergence. Through extensive experiments, we show that our proposed algorithm significantly outperforms the state-of-the-art algorithms in terms of the recommendation intensity.

学术社交网络分析引起了广泛的关注。对于这样一个网络中的每个研究人员，他/她都有几个研究兴趣。我们将这些具有共同利益的研究人员视为一个研究社区。对于每个社区，它可能对其他社区的研究人员都有吸引力。在本文中，我们研究了一个新的有趣问题：哪个是学术社交网络中最吸引人的研究社区？在这里，有吸引力的研究社区是那些潜在的有价值且日益流行的社区，与热门社区不同。为了解决这个问题，我们首先提取有吸引力的研究社区的内部和外部特征。内部特征是指研究社区中主题的新颖性，外部特征是指研究人员在社区之间的过渡。直觉上，一个具有新颖主题的社区吸引了其他研究社区的研究人员，可以认为是有吸引力的社区。基于提取的特征，我们设计了一种新颖的“有吸引力的研究社区排名”（ARTRank）算法来对研究社区进行排名。该算法的核心思想在于对每个社区进行两次测量：正面度得分和负面度得分，分别从关注度和关注度两个方面衡量社区的吸引力。与HITS相似，这两个分数以迭代方式计算，直到收敛为止。通过广泛的实验，