arXiv subjects

Arijit Khan

Publications and source records attributed to Arijit Khan.

47 records · Page 3Linked to original sources

arXiv2020

Semantic Guided and Response Times Bounded Top-k Similarity Search over Knowledge Graphs

Recently, graph query is widely adopted for querying knowledge graphs. Given a query graph $G_Q$, the graph query finds subgraphs in a knowledge graph $G$ that exactly or approximately match $G_Q$. We face two challenges on graph query: (1) the structural gap between $G_Q$ and the predefined schema in $G$ causes mismatch with query graph, (2) users cannot view the answers until the graph query terminates, leading to a longer system response time (SRT). In this paper, we propose a semantic-guided and response-time-bounded graph query to return the top-k answers effectively and efficiently. We leverage a knowledge graph embedding model to build the semantic graph $SG_Q$, and we define the path semantic similarity ($pss$) over $SG_Q$ as the metric to evaluate the answer's quality. Then, we propose an A* semantic search on $SG_Q$ to find the top-k answers with the greatest $pss$ via a heuristic $pss$ estimation. Furthermore, we make an approximate optimization on A* semantic search to allow users to trade off the effectiveness for SRT within a user-specific time bound. Extensive experiments over real datasets confirm the effectiveness and efficiency of our solution.

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arXiv2019

Maximizing Contrasting Opinions in Signed Social Networks

The classic influence maximization problem finds a limited number of influential seed users in a social network such that the expected number of influenced users in the network, following an influence cascade model, is maximized. The problem has been studied in different settings, with further generalization of the graph structure, e.g., edge weights and polarities, target user categories, etc. In this paper, we introduce a unique influence diffusion scenario involving a population that split into two distinct groups, with opposing views. We aim at finding the top-$k$ influential seed nodes so to simultaneously maximize the adoption of two distinct, antithetical opinions in the two groups, respectively. Efficiently finding such influential users is essential in a wide range of applications such as increasing voter engagement and turnout, steering public debates and discussions on societal issues with contentious opinions. We formulate this novel problem with the voter model to simulate opinion diffusion and dynamics, and then design a linear-time and exact algorithm COSiNeMax, while also investigating the long-term opinion characteristics in the network. Our experiments with several real-world datasets demonstrate the effectiveness and efficiency of the proposed algorithm, compared to various baselines.

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arXiv2019

Composite Hashing for Data Stream Sketches

In rapid and massive data streams, it is often not possible to estimate the frequency of items with complete accuracy. To perform the operation in a reasonable amount of space and with sufficiently low latency, approximated methods are used. The most common ones are variations of the Count-Min sketch. By using multiple hash functions, they summarize massive streams in sub-linear space. In reality, data item ids or keys can be modular, e.g., a graph edge is represented by source and target node ids, a 32-bit IP address is composed of four 8-bit words, a web address consists of domain name, domain extension, path, and filename, among many others. In this paper, we investigate the modularity property of item keys, and systematically develop more accurate, composite hashing strategies, such as employing multiple independent hash functions that hash different modules in a key and their combinations separately, instead of hashing the entire key directly into the sketch. However, our problem of finding the best hashing strategy is non-trivial, since there are exponential number of ways to combine the modules of a key before they can be hashed into the sketch. Moreover, given a fixed size allocated for the entire sketch, it is hard to find the optimal range of all hash functions that correspond to different modules and their combinations. We solve both these problems with extensive theoretical analysis, and perform thorough experiments with real-world datasets to demonstrate the accuracy and efficiency of our proposed method, MOD-Sketch.

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arXiv2019

Distance-generalized Core Decomposition

The $k$-core of a graph is defined as the maximal subgraph in which every vertex is connected to at least $k$ other vertices within that subgraph. In this work we introduce a distance-based generalization of the notion of $k$-core, which we refer to as the $(k,h)$-core, i.e., the maximal subgraph in which every vertex has at least $k$ other vertices at distance $\leq h$ within that subgraph. We study the properties of the $(k,h)$-core showing that it preserves many of the nice features of the classic core decomposition (e.g., its connection with the notion of distance-generalized chromatic number) and it preserves its usefulness to speed-up or approximate distance-generalized notions of dense structures, such as $h$-club. Computing the distance-generalized core decomposition over large networks is intrinsically complex. However, by exploiting clever upper and lower bounds we can partition the computation in a set of totally independent subcomputations, opening the door to top-down exploration and to multithreading, and thus achieving an efficient algorithm.

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arXiv2019

An In-Depth Comparison of s-t Reliability Algorithms over Uncertain Graphs

Uncertain, or probabilistic, graphs have been increasingly used to represent noisy linked data in many emerging applications, and have recently attracted the attention of the database research community. A fundamental problem on uncertain graphs is the s-t reliability, which measures the probability that a target node t is reachable from a source node s in a probabilistic (or uncertain) graph, i.e., a graph where every edge is assigned a probability of existence. Due to the inherent complexity of the s-t reliability estimation problem (#P-hard), various sampling and indexing based efficient algorithms were proposed in the literature. However, since they have not been thoroughly compared with each other, it is not clear whether the later algorithm outperforms the earlier ones. More importantly, the comparison framework, datasets, and metrics were often not consistent (e.g., different convergence criteria were employed to find the optimal number of samples) across these works. We address this serious concern by re-implementing six state-of-the-art s-t reliability estimation methods in a common system and code base, using several medium and large-scale, real-world graph datasets, identical evaluation metrics, and query workloads. Through our systematic and in-depth analysis of experimental results, we report surprising findings, such as many follow-up algorithms can actually be several orders of magnitude inefficient, less accurate, and more memory intensive compared to the ones that were proposed earlier. We conclude by discussing our recommendations on the road ahead.

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arXiv2018

Fast Influence Maximization in Dynamic Graphs: A Local Updating Approach

We propose a generalized framework for influence maximization in large-scale, time evolving networks. Many real-life influence graphs such as social networks, telephone networks, and IP traffic data exhibit dynamic characteristics, e.g., the underlying structure and communication patterns evolve with time. Correspondingly, we develop a dynamic framework for the influence maximization problem, where we perform effective local updates to quickly adjust the top-k influencers, as the structure and communication patterns in the network change. We design a novel N-Family approach (N=1, 2, 3, ...) based on the maximum influence arborescence (MIA) propagation model with approximation guarantee of (1-1/e). We then develop heuristic algorithms by extending the N-Family approach to other information propagation models (e.g., independent cascade, linear threshold) and influence maximization algorithms (e.g., CELF, reverse reachable sketch). Based on a detailed empirical analysis over several real-world, dynamic, and large-scale networks, we find that our proposed solution, N-Family improves the updating time of the top-k influencers by 1-2 orders of magnitude, compared to state-of-the-art algorithms, while ensuring similar memory usage and influence spreads.

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arXiv2018

Conditional Reliability in Uncertain Graphs

Network reliability is a well-studied problem that requires to measure the probability that a target node is reachable from a source node in a probabilistic (or uncertain) graph, i.e., a graph where every edge is assigned a probability of existence. Many approaches and problem variants have been considered in the literature, all assuming that edge-existence probabilities are fixed. Nevertheless, in real-world graphs, edge probabilities typically depend on external conditions. In metabolic networks a protein can be converted into another protein with some probability depending on the presence of certain enzymes. In social influence networks the probability that a tweet of some user will be re-tweeted by her followers depends on whether the tweet contains specific hashtags. In transportation networks the probability that a network segment will work properly or not might depend on external conditions such as weather or time of the day. In this paper we overcome this limitation and focus on conditional reliability, that is assessing reliability when edge-existence probabilities depend on a set of conditions. In particular, we study the problem of determining the k conditions that maximize the reliability between two nodes. We deeply characterize our problem and show that, even employing polynomial-time reliability-estimation methods, it is NP-hard, does not admit any PTAS, and the underlying objective function is non-submodular. We then devise a practical method that targets both accuracy and efficiency. We also study natural generalizations of the problem with multiple source and target nodes. An extensive empirical evaluation on several large, real-life graphs demonstrates effectiveness and scalability of the proposed methods.

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arXiv2017

On Smart Query Routing: For Distributed Graph Querying with Decoupled Storage

We study online graph queries that retrieve nearby nodes of a query node from a large network. To answer such queries with high throughput and low latency, we partition the graph and process the data in parallel across a cluster of servers. State-of-the-art distributed graph querying systems place each graph partition on a separate server, where query answering over that partition takes place. This design has two major disadvantages. First, the router needs to maintain a fixed routing table. Hence, these systems are less flexible with respect to query routing, fault tolerance, and graph updates. Second, the graph data must be partitioned such that the workload across the servers is balanced, and the inter-machine communication is minimized. In addition, it is required to update the existing partitions based on workload changes over graph nodes. However, graph partitioning, online monitoring of workloads, and dynamically updating the graph partitions are expensive. In this work, we mitigate both these problems by decoupling graph storage from query processors, and by developing smart routing strategies that improve the cache locality in query processors. Since a query processor is no longer assigned any fixed part of the graph, it is equally capable of handling any request, thus facilitating load balancing and fault tolerance. On the other hand, due to our smart routing strategies, query processors can effectively leverage their cache contents, reducing the overall impact of how the graph is partitioned across storage servers. A detailed experimental evaluation with several real-world, large graph datasets demonstrates that our proposed framework, gRouting - even with simple hash partitioning of the data - achieves up to an order of magnitude better query throughput compared to existing graph querying systems that employ expensive graph partitioning and re-partitioning strategies.

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arXiv2017

Select Your Questions Wisely: For Entity Resolution With Crowd Errors

Crowdsourcing is becoming increasingly important in entity resolution tasks due to their inherent complexity such as clustering of images and natural language processing. Humans can provide more insightful information for these difficult problems compared to machine-based automatic techniques. Nevertheless, human workers can make mistakes due to lack of domain expertise or seriousness, ambiguity, or even due to malicious intents. The state-of-the-art literature usually deals with human errors via majority voting or by assigning a universal error rate over crowd workers. However, such approaches are incomplete, and often inconsistent, because the expertise of crowd workers are diverse with possible biases, thereby making it largely inappropriate to assume a universal error rate for all workers over all crowdsourcing tasks. To this end, we mitigate the above challenges by considering an uncertain graph model, where the edge probability between two records A and B denotes the ratio of crowd workers who voted Yes on the question if A and B are same entity. In order to reflect independence across different crowdsourcing tasks, we apply the well-established notion of possible worlds, and develop parameter-free algorithms both for next crowdsourcing, as well as for entity resolution problems. In particular, using our framework, the problem of entity resolution becomes equivalent to finding the maximum-likelihood clustering; whereas for the next crowdsourcing, we identify the record pair that maximally increases the reliability of the maximum-likelihood clustering. Based on detailed empirical analysis over real-world datasets, we find that our proposed solution, PERC (probabilistic entity resolution with imperfect crowd) improves the quality by 15% and reduces the overall cost by 50% for the crowdsourcing-based entity resolution problem.

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arXiv2016

Vertex-Centric Graph Processing: The Good, the Bad, and the Ugly

We study distributed graph algorithms that adopt an iterative vertex-centric framework for graph processing, popularized by the Google's Pregel system. Since then, there are several attempts to implement many graph algorithms in a vertex-centric framework, as well as efforts to design optimization techniques for improving the efficiency. However, to the best of our knowledge, there has not been any systematic study to compare these vertex-centric implementations with their sequential counterparts. Our paper addresses this gap in two ways. (1) We analyze the computational complexity of such implementations with the notion of time-processor product, and benchmark several vertex-centric graph algorithms whether they perform more work with respect to their best-known sequential solutions. (2) Employing the concept of balanced practical Pregel algorithms, we study if these implementations suffer from imbalanced workload and large number of iterations. Our findings illustrate that with the exception of Euler tour tree algorithm, all other algorithms either perform more work than their best-known sequential approach, or suffer from imbalanced workload/ large number of iterations, or even both. We also emphasize on graph algorithms that are fundamentally difficult to be expressed in vertex-centric frameworks, and conclude by discussing the road ahead for distributed graph processing.

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arXiv2013

Querying Knowledge Graphs by Example Entity Tuples

We witness an unprecedented proliferation of knowledge graphs that record millions of entities and their relationships. While knowledge graphs are structure-flexible and content rich, they are difficult to use. The challenge lies in the gap between their overwhelming complexity and the limited database knowledge of non-professional users. If writing structured queries over simple tables is difficult, complex graphs are only harder to query. As an initial step toward improving the usability of knowledge graphs, we propose to query such data by example entity tuples, without requiring users to form complex graph queries. Our system, GQBE (Graph Query By Example), automatically derives a weighted hidden maximal query graph based on input query tuples, to capture a user's query intent. It efficiently finds and ranks the top approximate answer tuples. For fast query processing, GQBE only partially evaluates query graphs. We conducted experiments and user studies on the large Freebase and DBpedia datasets and observed appealing accuracy and efficiency. Our system provides a complementary approach to the existing keyword-based methods, facilitating user-friendly graph querying. To the best of our knowledge, there was no such proposal in the past in the context of graphs.

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