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Searching for relevant literature is a fundamental part of academic research. The search for relevant literature is becoming a more difficult and time-consuming task as millions of articles are published each year. As a solution, recommendation systems for academic papers attempt to help researchers find relevant papers quickly. This paper focuses on graph-based recommendation systems for academic papers using citation networks. This type of paper recommendation system leverages a graph of papers linked by citations to create a list of relevant papers. In this study, we explore recommendation systems for academic papers using citation networks incorporating citation relations. We define citation relation based on the number of times the origin paper cites the reference paper, and use this citation relation to measure the strength of the relation between the papers. We created a weighted network using citation relation as citation weight on edges. We evaluate our proposed method on a real-world publication data set, and conduct an extensive comparison with three state-of-the-art baseline methods. Our results show that citation network-based recommendation systems using citation weights perform better than the current methods. 

We consider the community search problem defined upon a large graph G: given a query vertex q in G, to find as output all the densely connected subgraphs of G, each of which contains the query v. As an online, query-dependent variant of the well-known community detection problem, community search enables personalized community discovery that has found widely varying applications in real-world, large-scale graphs. In this paper, we study the community search problem in the truss-based model aimed at discovering all dense and cohesive k-truss communities to which the query vertex q belongs. We introduce a novel equivalence relation, k-truss equivalence, to model the intrinsic density and cohesiveness of edges in k-truss communities. Consequently, all the edges of G can be partitioned to a series of k-truss equivalence classes that constitute a space-efficient, truss-preserving index structure, EquiTruss. Community search can be henceforth addressed directly upon EquiTruss without repeated, time-demanding accesses to the original graph, G, which proves to be theoretically optimal. In addition, EquiTruss can be efficiently updated in a dynamic fashion when G evolves with edge insertion and deletion. Experimental studies in real-world, large-scale graphs validate the efficiency and effectiveness of EquiTruss, which has achieved at least an order of magnitude speedup in community search over the state-of-the-art method, TCP-Index. 

Graph clustering is a fundamental problem in social network analysis, the goal of which is to group vertices of a graph into a series of densely knitted clusters with each cluster well separated from all the others. Classical graph clustering methods take advantage of the graph topology to model and quantify vertex proximity. With the proliferation of rich graph contents, such as user profiles in social networks, and gene annotations in protein interaction networks, it is essential to consider both the structure and content information of graphs for high-quality graph clustering. In this paper, we propose a graph embedding approach to clustering content-enriched graphs. The key idea is to embed each vertex of a graph into a continuous vector space where the localized structural and attributive information of vertices can be encoded in a unified, latent representation. Specifically, we quantify vertex-wise attribute proximity into edge weights, and employ truncated, attribute-aware random walks to learn the latent representations for vertices. We evaluate our attribute-aware graph embedding method in real-world attributed graphs, and the results demonstrate its effectiveness in comparison with state-of-the-art algorithms.