HubPPR

Considerable attention has been given to processing graph data in recent years. An efficient method for computing the node proximity is one of the most challenging problems for many applications such as recommendation systems and social networks. Regarding large-scale, mutable datasets and user queries, top-k query processing has gained significant interest. This paper presents a novel method to find top-k answers in a node proximity search based on the well-known measure, Personalized PageRank (PPR). First, we introduce a distribution state transition graph (DSTG) to depict iterative steps for solving the PPR equation. Second, we propose a weight distribution model of a DSTG to capture the states of intermediate PPR scores and their distribution. Using a DSTG, we can selectively follow and compare multiple random paths with different lengths to find the most promising nodes. Moreover, we prove that the results of our method are equivalent to the PPR results. Comparative performance studies using two real datasets clearly show that our method is practical and accurate.

Personalized PageRank (PPR) is an important variation of PageRank, which is a widely applied popularity measure for Web search. Unlike the original PageRank, PPR is a node proximity measure that represents the degree of closeness among multiple nodes within a graph. It is also widely applied to diverse domains, such as information retrieval, recommendations, and knowledge discovery, due to its theoretical simplicity and flexibility. However, computing PPR in large graphs using naïve algorithms such as iterative matrix multiplication and matrix inversion is not fast enough for many of these applications. Therefore, devising efficient PPR algorithms has been one of the most important subjects in large-scale graph processing. In this paper, we review the algorithms for efficient PPR computations, organizing them into five categories based on their core ideas. Along with detailed explanations including recent advances and their applications, we provide a multifaceted comparison of the algorithms.

Analysis of large graphs is critical to the ongoing growth of search engines and social networks. One class of queries centers around node affinity, often quantified by random-walk distances between node pairs, including hitting time, commute time, and personalized PageRank (PPR). Despite the potential of these "metrics," they are rarely, if ever, used in practice, largely due to extremely high computational costs. In this paper, we investigate methods to scalably and efficiently compute random-walk distances, by "embedding" graphs and distances into points and distances in geometric coordinate spaces. We show that while existing graph coordinate systems (GCS) can accurately estimate shortest path distances, they produce significant errors when embedding random-walk distances. Based on our observations, we propose a new graph embedding system that explicitly accounts for per-node graph properties that affect random walk. Extensive experiments on a range of graphs show that our new approach can accurately estimate both symmetric and asymmetric random-walk distances. Once a graph is embedded, our system can answer queries between any two nodes in 8 microseconds, orders of magnitude faster than existing methods. Finally, we show that our system produces estimates that can replace ground truth in applications with minimal impact on application output.

The large volume of data available in location-based social networks (LBSNs) enables Point-of-Interest (POI) recommendation services. On another hand, the heterogeneous information (e.g., user check-in records, geographical features of POIs, social network and user reviews) imposes great challenges on effective POI recommendation. In this paper, we focus on leveraging such rich information in an integrated manner to improve POI recommendation performance. We exploit not only the geographical and social information, but also aspects extracted from user reviews to better model users' preferences. More specifically, to fully utilize various types of information, we construct a novel heterogeneous graph, Aspect-aware Geo-Social influence Graph (AGSG), by fusing various relations among the three types of nodes, i.e., users, POIs and aspects. The personalized POI recommendation task is then transformed as a graph node ranking problem in AGSG. We design a graph-based recommendation algorithm based on both personalized PageRank (PPR) and meta paths, to fully exploit the heterogeneous graph structure as well as to capture the semantic relations among the various nodes. Experiments on three real-world datasets show that our proposed approach outperforms the state-of-art methods.

The random walk process on network data is a widely-used approach for network representation learning. However, we argue that the sampling of node sequences and the subsampling for the Skip-gram’s contexts have two drawbacks. One is less possible to precisely find the most correlated context nodes for every central node with only uniform graph search. The other is not easily controlled due to the expensive cost of hyperparameter tuning. Such two drawbacks lead to higher training cost and lower accuracy due to abundant and irrelevant samples. To solve these problems, we compute the adaptive probability of random walk based on Personalized PageRank (PPR), and propose an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adaptive SKip-gram</i> (ASK) model without using complicated sampling process and negative sampling. We utilize <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -most important neighbors for positive samples selection, and attach their corresponding PPR probability into the objective function. Based on benchmark datasets with three citation networks and three social networks, we demonstrate the improvement of our ASK model for network representation learning in tasks of link prediction, node classification, and embedding visualization. The results achieve more effective performance and efficient learning time.

We consider stochastic transition matrices from large social and information networks. For these matrices, we describe and evaluate three fast methods to estimate one column of the matrix exponential. The methods are designed to exploit the properties inherent in social networks, such as a power-law degree distribution. Using only this property, we prove that one of our three algorithms has a sublinear runtime. We present further experimental evidence showing that all three of them run quickly on social networks with billions of edges, and they accurately identify the largest elements of the column.

partitioning graphs at extent is a critical contest for several solicitations that incorporate provision a graph transversely disk, technologies, or data centers. Graph partitioning is an exact fine, considered difficult with satirical collected works, but surviving algorithms classically cannot measure to billions of edges, or cannot deliver assurances about partition bulks. In this work, we announce an effective algorithm, composed label propagation, for exactly partitioning immense graphs while hungrily exploiting edge vicinity, the expanse of edges that are allocated to the equal sliver of a partition. By merging the computational competence of label propagation where nodes are iteratively relabeled to the similar ‘label’ as the variety of their graph neighbors' - with the assurances of inhibited optimization -managing the propagation by an undeviating platform obliging the partition scopes - our algorithm types it virtually imaginable to divide graphs with billions of edges. Our algorithm is attracted by the competition of finishing Graph assessments in a detached structure. Because this requires conveying each node in a graph to a physical machine with memory boundaries, it is disapprovingly essential to guarantee the resultant partition debris do not exceed several single machines. We estimate our algorithm for its dividing performance on the social graph, and also learning its enactment when dividing Facebook's ‘People You May Know’ service (PYMK), the dispersed scheme answerable for the article abstraction and the standing of the friends-of-friends of all vigorous Facebook users. In a live placement, we observed typical interrogation times and average network circulation stages that were 50.5% and 37.1% (respectively) when matched to the earlier naive random sharding.

Estimating cascade size and nodes' influence is a fundamental task in social, technological, and biological networks. Yet this task is extremely challenging due to the sheer size and the structural heterogeneity of networks. We investigate a new influence measure, termed outward influence (OI), defined as the (expected) number of nodes that a subset of nodes S will activate, excluding the nodes in S . Thus, OI equals, the de facto standard measure, influence spread of S minus | S |. OI is not only more informative for nodes with small influence, but also, critical in designing new effective sampling and statistical estimation methods. Based on OI, we propose SIEA/SOIEA, novel methods to estimate influence spread/outward influence at scale and with rigorous theoretical guarantees . The proposed methods are built on two novel components 1) IICP an important sampling method for outward influence; and 2) RSA, a robust mean estimation method that minimize the number of samples through analyzing variance and range of random variables. Compared to the state-of-the art for influence estimation, SIEA is Ω(log 4 n ) times faster in theory and up to several orders of magnitude faster in practice. For the first time, influence of nodes in the networks of billions of edges can be estimated with high accuracy within a few minutes. Our comprehensive experiments on real-world networks also give evidence against the popular practice of using a fixed number, e.g. 10K or 20K, of samples to compute the "ground truth" for influence spread.

This book lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks. Representing much of the foundational research in this field, the book develops scalable algorithms that exploit the graphlike properties underlying personalized search and reputation management, and delves into realistic scenarios regarding Web-scale data. Sep Kamvar focuses on eigenvector-based techniques in Web search, introducing a personalized variant of Google's PageRank algorithm, and he outlines algorithms--such as the now-famous quadratic extrapolation technique--that speed up computation, making personalized PageRank feasible. Kamvar suggests that Power Method-related techniques ultimately should be the basis for improving the PageRank algorithm, and he presents algorithms that exploit the convergence behavior of individual components of the PageRank vector. Kamvar then extends the ideas of reputation management and personalized search to distributed networks like peer-to-peer and social networks. He highlights locality and computational considerations related to the structure of the network, and considers such unique issues as malicious peers. He describes the EigenTrust algorithm and applies various PageRank concepts to P2P settings. Discussion chapters summarizing results conclude the book's two main sections. Clear and thorough, this book provides an authoritative look at central innovations in search for all of those interested in the subject.

Advertising in social network has become a multi-billion-dollar industry. A main challenge is to identify key influencers who can effectively contribute to the dissemination of information. Although the influence maximization problem, which finds a seed set of k most influential users based on certain propagation models, has been well studied, it is not target-aware and cannot be directly applied to online advertising. In this paper, we propose a new problem, named Keyword-Based Targeted Influence Maximization (KB-TIM), to find a seed set that maximizes the expected influence over users who are relevant to a given advertisement. To solve the problem, we propose a sampling technique based on weighted reverse influence set and achieve an approximation ratio of (1 − 1/ e − ε ). To meet the instant-speed requirement, we propose two disk-based solutions that improve the query processing time by two orders of magnitude over the state-of-the-art solutions, while keeping the theoretical bound. Experiments conducted on two real social networks confirm our theoretical findings as well as the efficiency. Given an advertisement with 5 keywords, it takes only 2 seconds to find the most influential users in a social network with billions of edges.

LOSC: A locality-optimized subgraph construction scheme for out-of-core graph processing

Background/Objectives: PageRank given by Brin and Larry in 1998, emerged as a dominant link analysis method used by web search engines for ranking of its search results. Efficient and fast computation of PageRank values for prodigious web graphs is indeed an important issue for web search engines today. Recognizing and fighting with spam web pages is also considered to be another necessary issue in web searching. Methods/Statistical Analysis: In this paper, we have proposed an efficient and accelerated parallel computation of PageRank scores on Graphics Processing Units (GPUs) which uses non even distribution of PageRank values. This work is experimented on datasets taken from Stanford Large Network Dataset Collection, on a system equipped with NVIDIA Quadro 2000 Graphics card using CUDA programming language. Findings: The proposed work has a speed up of 3.22 to 7.5 and is also capable of dealing with spam web pages. Application: The proposed algorithm helps in detecting spam web pages.

The proliferation of smartphones and location-based social networks yield workloads where sets of points of interest are updated according to streaming spatio-textual objects and are queried simultaneously according to their spatial and textual attributes. While indexing is needed to enable efficient querying, the magnitude of updates in such settings call for new indexing techniques. Targeting such settings, we study the problem of approximate top-k spatio-textual object retrieval, the idea being to achieve improved query, update, and space performance by sacrificing query quality only slightly. Specifically, we propose an indexing technique for points of interests in streaming settings that integrates sketching along with a series of optimization techniques. An experimental study demonstrates that our technique achieves over 88% accuracy and 1–2 orders of magnitude lower query times and storage sizes than existing indexing techniques.

Mobile phones are powerful tools to connect people. The streams of Call Detail Records (CDR’s) generating from these devices provide a powerful abstraction of social interactions between individuals, representing social structures. Call graphs can be deduced from these CDRs, where nodes represent subscribers and edges represent the phone calls made. These graphs may easily reach millions of nodes and billions of edges. Besides being large-scale and generated in real-time, the underlying social networks are inherently complex and, thus, difficult to analyze. Conventional data analysis performed by telecom operators is slow, done by request and implies heavy costs in data warehouses. In face of these challenges, real-time streaming analysis becomes an ever increasing need to mobile operators, since it enables them to quickly detect important network events and optimize business operations. Sampling, together with visualization techniques, are required for online exploratory data analysis and event detection in such networks. In this chapter, we report the burgeoning body of research in network sampling, visualization of streaming social networks, stream analysis and the solutions proposed so far.

With the availability of multicore and manycore systems with large main memories, Symmetric Multiprocessors (SMPs) can process large scale problems such as graphs spanning millions of nodes and billions of edges which used to be the domain of large clusters. Due to the shared memory architecture of SMPs, we can apply fast, novel policies such as Transactional Memory (TM) to speed up the processing of such problems. Interestingly, most applications in bioinformatics, social networks, and cybersecurity can be represented as large graphs. However, these graphs are sparse in nature. Therefore, TM as a synchronization policy for critical sections can provide better performance since it performs better in low conflict scenarios. Many TM variants exist including: Software, Hardware and combinations of both. Furthermore, a TM can adapt to applications' behavior. In this paper, we introduce DAdHTM. A Dynamically Adaptive Hardware TM (DAdHTM) designed to adapt the HTM to the application's behavior. DAdHTM outperforms not only locks but also low overhead Software TM (STM) and even Adaptive Hybrid TM (DyAdHyTM) on a 48 core 64GB memory dual socket SMP machine. Our experiments show that DAdHTM has low overhead. It is competitive at core/thread counts below 24 (close to the best) and outperforms all other techniques at core/thread counts above 24 by up to 43%.