We consider in this paper the edge classification problem in networks, which is defined as follows. Given a graph-structured network G(N, A), where N is a set of vertices and A N ×N is a set of edges, in which a subset Al A of edges are properly labeled a priori, determine for those edges in Au = A\Al the edge labels which are unknown. The edge classification problem has numerous applications in graph mining and social network analysis, such as relationship discovery, categorization, and recommendation. Although the vertex classification problem has been well known and extensively explored in networks, edge classification is relatively unknown and in an urgent need for careful studies. In this paper, we present a series of efficient, neighborhood-based algorithms to perform edge classification in networks. To make the proposed algorithms scalable in large-scale networks, which can be either disk-resident or streamlike, we further devise efficient, cost-effective probabilistic edge classification methods without a significant compromise to the classification accuracy. We carry out experimental studies in a series of real-world networks, and the experimental results demonstrate both the effectiveness and efficiency of the proposed methods for edge classification in large networks.