Automatic feature learning algorithms are at the forefront of modern day machine learning research. We present a novel algorithm, supervised Q-walk, which applies Q-learning to generate random walks on graphs such that the walks prove to be useful for learning node features suitable for tackling with the node classification problem. We present another novel algorithm, k-hops neighborhood based confidence values learner, which learns confidence values of labels for unlabelled nodes in the network without first learning the node embedding. These confidence values aid in learning an apt reward function for Q-learning. We demonstrate the efficacy of supervised Q-walk approach over existing state-of-the-art random walk based node embedding learners in solving the single multi-label multi-class node classification problem using several real world datasets. Summarising, our approach represents a novel state-of-the-art technique to learn features, for nodes in networks, tailor-made for dealing with the node classification problem.
Graph Analytics field is pacing up due to the growth of large datasets in social network analysis @cite_21 @cite_22 @cite_4 , communication networks @cite_10 @cite_16 , etc. The area of node classification @cite_3 has been approached earlier from different perspectives like factorization based approaches, random walk based approaches, etc.
@cite_4 @cite_22 @cite_21 @cite_3 @cite_16 @cite_10
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Modern applications such as web knowledge base, network traffic monitoring and online social networks have made available an unprecedented amount of network data with rich types of interactions carrying multiple attributes, for instance, port number and time tick in the case of network traffic. The design of algorithms to leverage this structured relationship with the power of computing to assist researchers and practitioners for better understanding, exploration and navigation of this space of information has become a challenging, albeit rewarding, topic in social network analysis and data mining. The constantly growing scale and enriching genres of network data always demand higher levels of efficiency, robustness and generalizability where existing approaches with successes on small, homogeneous network data are likely to fall short. We introduce MultiAspectForensics, a handy tool to automatically detect and visualize novel sub graph patterns within a local community of nodes in a heterogenous network, such as a set of vertices that form a dense bipartite graph whose edges share exactly the same set of attributes. We apply the proposed method on three data sets from distinct application domains, present empirical results and discuss insights derived from these patterns discovered. Our algorithm, built on scalable tensor analysis procedures, captures spectral properties of network data and reveals informative signals for subsequent domain-specific study and investigation, such as suspicious port-scanning activities in the scenario of cyber-security monitoring. Detecting community structures in graphs is a well studied problem in graph data analytics. Unprecedented growth in graph structured data due to the development of the world wide web and social networks in the past decade emphasizes the need for fast graph data analytics techniques. In this paper we present a simple yet efficient approach to detect communities in large scale graphs by modifying the sequential Louvain algorithm for community detection. The proposed distributed memory parallel algorithm targets the costly first iteration of the initial method by parallelizing it. Experimental results on a MPI setup with 128 parallel processes shows that up to ≈5× performance improvement is achieved as compared to the sequential version while not compromising the correctness of the final result. Graph datasets with billions of edges, such as social and Web graphs, are prevalent. To be feasible, computation on such large graphs should scale linearly with graph size. All-distances sketches (ADSs) are emerging as a powerful tool for scalable computation of some basic properties of individual nodes or the whole graph. ADSs were first proposed two decades ago (Cohen 1994) and more recent algorithms include ANF (Palmer, Gibbons, and Faloutsos 2002) and hyperANF (Boldi, Rosa, and Vigna 2011). A sketch of logarithmic size is computed for each node in the graph and the computation in total requires only a near linear number of edge relaxations. From the ADS of a node, we can estimate neighborhood cardinalities (the number of nodes within some query distance) and closeness centrality. More generally we can estimate the distance distribution, effective diameter, similarities, and other parameters of the full graph. We make several contributions which facilitate a more effective use of ADSs for scalable analysis of massive graphs. We provide, for the first time, a unified exposition of ADS algorithms and applications. We present the Historic Inverse Probability (HIP) estimators which are applied to the ADS of a node to estimate a large natural class of queries including neighborhood cardinalities and closeness centralities. We show that our HIP estimators have at most half the variance of previous neighborhood cardinality estimators and that this is essentially optimal. Moreover, HIP obtains a polynomial improvement over state of the art for more general domain queries and the estimators are simple, flexible, unbiased, and elegant. The ADS generalizes Min-Hash sketches, used for approximating cardinality (distinct count) on data streams. We obtain lower bounds on Min-Hash cardinality estimation using classic estimation theory. We illustrate the power of HIP, both in terms of ease of application and estimation quality, by comparing it to the HyperLogLog algorithm ( 2007), demonstrating a significant improvement over this state-of-the-art practical algorithm. We also study the quality of ADS estimation of distance ranges, generalizing the near-linear time factor-2 approximation of the diameter. The analysis of data represented as graphs is common having wide scale applications from social networks to medical imaging. A popular analysis is to cut the graph so that the disjoint subgraphs can represent communities (for social network) or background and foreground cognitive activity (for medical imaging). An emerging setting is when multiple data sets (graphs) exist which opens up the opportunity for many new questions. In this paper we study two such questions: i) For a collection of graphs find a single cut that is good for all the graphs and ii) For two collections of graphs find a single cut that is good for one collection but poor for the other. We show that existing formulations of multiview, consensus and alternative clustering cannot address these questions and instead we provide novel formulations in the spectral clustering framework. We evaluate our approaches on functional magnetic resonance imaging (fMRI) data to address questions such as: "What common cognitive network does this group of individuals have?" and "What are the differences in the cognitive networks for these two groups?" We obtain useful results without the need for strong domain knowledge.