Research

There are increasing interests in learning low-dimensional and dense node representations from the network structure which is usually high-dimensional and sparse. However, most existing methods fail to consider semantic meanings of links. Different links may have different semantic meanings because the similarities between two nodes can be different, e.g., two nodes share common neighbors and two nodes share similar interests which are demonstrated in node-generated content. In this paper, the former type of links are referred to as structure-close links while the latter type are referred to as content-close links. These two types of links naturally indicate there are two types of characteristics that nodes expose in a social network. Hence, we propose to learn two representations for each node, and render each representation responsible for encoding the corresponding type of node characteristics, which is achieved by jointly embedding the network structure and inferring the type of each link. In the experiments, the proposed method is demonstrated to be more effective than five recent methods on four social networks through applications including visualization, link prediction and multi-label classification.