We argue that relationships between Web pages are functions of the user's intent. We identify a class of Web tasks - information-gathering - that can be facilitated by a search engine that provides links to pages which are related to the page the user is currently viewing. We define three kinds of intentional relationships that correspond to whether the user is a) seeking sources of information, b) reading pages which provide information, or c) surfing through pages as part of an extended information-gathering process. We show that these three relationships can be productively mined using a combination of textual and link information and provide three scoring mechanisms that correspond to them: SeekRel , FactRel and SurfRel . These scoring mechanisms incorporate both textual and link information. We build a set of capacitated subnetworks - each corresponding to a particular keyword - that mirror the interconnection structure of the World Wide Web. The scores are computed by computing flows on these subnetworks. The capacities of the links are derived from the hub and authority values of the nodes they connect, following the work of Kleinberg (1998) on assigning authority to pages in hyperlinked environments. We evaluated our scoring mechanism by running experiments on four data sets taken from the Web. We present user evaluations of the relevance of the top results returned by our scoring mechanisms and compare those to the top results returned by Google's Similar Pages feature, and the Companion algorithm proposed by Dean and Henzinger (1999).
In a different use of link structure related to our own Lu et. al. @cite_30 @cite_31 claimed that two pages were said to be similar if flow could be routed from one of them to the other. However, unlike our work, their capacity assignments were not based on any notion of authority. To the best of our knowledge this is the only other mention of using flow to score similarity in the literature.
@cite_30 @cite_31
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In this work, we address the problem of joint modeling of text and citations in the topic modeling framework. We present two different models called the Pairwise-Link-LDA and the Link-PLSA-LDA models. The Pairwise-Link-LDA model combines the ideas of LDA [4] and Mixed Membership Block Stochastic Models [1] and allows modeling arbitrary link structure. However, the model is computationally expensive, since it involves modeling the presence or absence of a citation (link) between every pair of documents. The second model solves this problem by assuming that the link structure is a bipartite graph. As the name indicates, Link-PLSA-LDA model combines the LDA and PLSA models into a single graphical model. Our experiments on a subset of Citeseer data show that both these models are able to predict unseen data better than the baseline model of Erosheva and Lafferty [8], by capturing the notion of topical similarity between the contents of the cited and citing documents. Our experiments on two different data sets on the link prediction task show that the Link-PLSA-LDA model performs the best on the citation prediction task, while also remaining highly scalable. In addition, we also present some interesting visualizations generated by each of the models. In this paper, we consider the problem of approximately aligning matching two graphs. Given two graphs (G_ 1 =(V_ 1 ,E_ 1 ) ) and (G_ 2 =(V_ 2 ,E_ 2 ) ), the objective is to map nodes (u, v G_1 ) to nodes (u',v' G_2 ) such that when u, v have an edge in (G_1 ), very likely their corresponding nodes (u', v' ) in (G_2 ) are connected as well. This problem with subgraph isomorphism as a special case has extra challenges when we consider matching complex networks exhibiting the small world phenomena. In this work, we propose to use ‘Ricci flow metric’, to define the distance between two nodes in a network. This is then used to define similarity of a pair of nodes in two networks respectively, which is the crucial step of network alignment. Specifically, the Ricci curvature of an edge describes intuitively how well the local neighborhood is connected. The graph Ricci flow uniformizes discrete Ricci curvature and induces a Ricci flow metric that is insensitive to node edge insertions and deletions. With the new metric, we can map a node in (G_1 ) to a node in (G_2 ) whose distance vector to only a few preselected landmarks is the most similar. The robustness of the graph metric makes it outperform other methods when tested on various complex graph models and real world network data sets (Emails, Internet, and protein interaction networks) (The source code of computing Ricci curvature and Ricci flow metric are available: https: github.com saibalmars GraphRicciCurvature). In this paper, we present an approach to comparing control flow graphs of binary programs by matching their basic blocks. We first set up an initial match and propagate it to reach a stable state. We consider the matched pairs to identify overall similarities. To evaluate the proposed method, we perform experiments on real-world Java applications, and compare their performance with previous structural matching method. In the experimental results, the proposed method shows more reliable results than previous method at distinguishing similar control flow graphs. Introduction. The problem discussed in this paper was formulated by T. Harris as follows: "Consider a rail network connecting two cities by way of a number of intermediate cities, where each link of the network has a number assigned to it representing its capacity. Assuming a steady state condition, find a maximal flow from one given city to the other." While this can be set up as a linear programming problem with as many equations as there are cities in the network, and hence can be solved by the simplex method (1), it turns out that in the cases of most practical interest, where the network is planar in a certain restricted sense, a much simpler and more efficient hand computing procedure can be described. In §1 we prove the minimal cut theorem, which establishes that an obvious upper bound for flows over an arbitrary network can always be achieved. The proof is non-constructive. However, by specializing the network (§2), we obtain as a consequence of the minimal cut theorem an effective computational scheme. Finally, we observe in §3 the duality between the capacity problem and that of finding the shortest path, via a network, between two given points.