Network embedding, which aims to learn the low-dimensional representations of vertices, is an important task and has attracted considerable research efforts recently. In real world, networks, like social network and biological networks, are dynamic and evolving over time. However, almost all the existing network embedding methods focus on static networks while ignore network dynamics. In this paper, we present a novel representation learning approach, DynamicTriad, to preserve both structural information and evolution patterns of a given network. The general idea of our approach is to impose triad, which is a group of three vertices and is one of the basic units of networks. In particular, we model how a closed triad, which consists of three vertices connected with each other, develops from an open triad that has two of three vertices not connected with each other. This triadic closure process is a fundamental mechanism in the formation and evolution of networks, thereby makes our model being able to capture the network dynamics and to learn representation vectors for each vertex at different time steps. Experimental results on three real-world networks demonstrate that, compared with several state-of-the-art techniques, DynamicTriad achieves substantial gains in several application scenarios. For example, our approach can effectively be applied and help to identify telephone frauds in a mobile network, and to predict whether a user will repay her loans or not in a loan network.

We investigate the effect of relational dialogue on creating rapport and exerting social influence in human-robot conversation, by comparing interactions with and without a relational component, and with different agent types. Human participants interact with two agents — a Nao robot and a virtual human — in four dialogue scenarios: one involving building familiarity, and three involving sharing information and persuasion in item-ranking tasks. Results show that both agents influence human decision-making; people prefer interacting with the robot, feel higher rapport with the robot, and believe the robot has more influence; and that objective influence of the agent on the person is increased by building familiarity, but is not significantly different between the agents.