Top-k Nodes Research Articles

An efficient method for top-k graph based node matching

Graph Pattern Matching (GPM) is to find those subgraphs that match a given pattern graph. In many applications, users are interested in the top-k nodes that matches the label of a specific node, (n...

Approach to Mine the Modularity of Software Network Based on the Most Vital Nodes

Analysis on the modularity of software network plays a critical role in the detection of software vulnerabilities and in the improvement of software stability, reliability, and robustness. This paper intends to propose a method based on the most vital nodes to analyze the modularity of software of different versions. To this end, it first tracked software dynamic execution traces to build a dynamic software network model , and then it mapped these traces to a complex network of a dynamic invoke software network . Second, it identified the most vital nodes in two steps, namely the calInitialWeightForNode and calFinalWeightForNode in order to compute the initial weights and the final weights of nodes iteratively. Third, it mined top-k nodes as the original communities to create a framework for detecting new community, and expanded these nodes to the community by the algorithm expandTheCommunity and evaluated the results with criterion ${Q}$ . Finally, it calculated the modularity of software of different versions. Experimental results show that the most vital nodes are reasonable to be taken in comparison with other ranking measures, and that the analysis approach to the modularity of software network is effective in evaluating software community structure, and can help assist the developer to refactor the software and improve the software quality.

Boundary Node Detection and Unfolding of Complex Non-Convex Ad Hoc Networks

Complex non-convex ad hoc networks (CNCAH) contain intersecting polygons and edges. In many instances, the layouts of these networks are not entirely convex in shape. In this article, we propose a Kamada-Kawai-based algorithm called W-KK-MS for boundary node detection problems, which is capable of aligning node positions while achieving high sensitivity, specificity, and accuracy in producing a visual drawing from the input network topology. The algorithm put forward in this article selects and assigns weights to top- k nodes in each iteration to speed up the updating process of nodes. We also propose a novel approach to detect and unfold stacked regions in CNCAH networks. Experimental results show that the proposed algorithms can achieve fast convergence on boundary node detection in CNCAH networks and are able to successfully unfold stacked regions. The design and implementation of a prototype system called ELnet for analyzing CNCAH networks is also described in this article. The ELnet system is capable of generating synthetic networks for testing, integrating with force-directed algorithms, and visualizing and analyzing algorithms’ outcomes.

ActiveDBC: learning Knowledge-based Information propagation in mobile social networks

Due to fast-growing mobile devices usage such as smartphones and wearable devices, the rapid information propagation in the Mobile Social Networks environment is very important. In particular, information transmission of people with repeated daily patterns in complex areas such as big cities requires a very meaningful analysis. We address the problem of identifying a key player who can quickly propagate the information to the whole network. This problem, in other words, often refer as the information propagation problem. In this research, we selected the top-k influential nodes to learn the knowledge-based movements of people by using a Markov chain process in a real-life environment. Subsequently, their movement probabilities according to virtual regions were used to ensure appropriate clustering based on the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Since moving patterns in a university campus data have a dense collection of people, the DBSCAN algorithm was useful for producing very dense groupings. After clustering, we also elected the top-k influential nodes based on the results learned from the score of each node according to groups. We determined the rate at which information spreads by using trace data from a real network. Our experiments were conducted in the Opportunistic Network Environment simulator. The results showed that the proposed method has outstanding performance for the level of spreading time in comparison to other methods such as Naive, Degree, and K-means. Furthermore, we compared the performance of RandomDBC with that of ActiveDBC, proving that the latter method was important to extract the influential top-k nodes, and showed superior performance.

Identification of top-K nodes in large networks using Katz centrality

Network theory concepts form the core of algorithms that are designed to uncover valuable insights from various datasets. Especially, network centrality measures such as Eigenvector centrality, Katz centrality, PageRank centrality etc., are used in retrieving top-K viral information propagators in social networks,while web page ranking in efficient information retrieval, etc. In this paper, we propose a novel method for identifying top-K viral information propagators from a reduced search space. Our algorithm computes the Katz centrality and Local average centrality values of each node and tests the values against two threshold (constraints) values. Only those nodes, which satisfy these constraints, form the search space for top-K propagators. Our proposed algorithm is tested against four datasets and the results show that the proposed algorithm is capable of reducing the number of nodes in search space at least by 70%. We also considered the parameter (alpha and beta) dependency of Katz centrality values in our experiments and established a relationship between the alpha values, number of nodes in search space and network characteristics. Later, we compare the top-K results of our approach against the top-K results of degree centrality.

A Fast and Efficient Algorithm for Mining Top-k Nodes in Complex Networks

One of the key problems in social network analysis is influence maximization, which has great significance both in theory and practical applications. Given a complex network and a positive integer k, and asks the k nodes to trigger the largest expected number of the remaining nodes. Many mature algorithms are mainly divided into propagation-based algorithms and topology- based algorithms. The propagation-based algorithms are based on optimization of influence spread process, so the influence spread of them significantly outperforms the topology-based algorithms. But these algorithms still takes days to complete on large networks. Contrary to propagation based algorithms, the topology-based algorithms are based on intuitive parameter statistics and static topology structure properties. Their running time are extremely short but the results of influence spread are unstable. In this paper, we propose a novel topology-based algorithm based on local index rank (LIR). The influence spread of our algorithm is close to the propagation-based algorithm and sometimes over them. Moreover, the running time of our algorithm is millions of times shorter than that of propagation-based algorithms. Our experimental results show that our algorithm has a good and stable performance in IC and LT model.

Identifying Top-k Most Influential Nodes by using the Topological Diffusion Models in the Complex Networks

Social networks are sub-set of complex networks, where users are defined as nodes, and the connections between users are edges. One of the important issues concerning social network analysis is identifying influential and penetrable nodes. Centrality is an important method among many others practiced for identification of influential nodes. Centrality criteria include degree centrality, betweenness centrality, closeness centrality, and Eigenvector centrality; all of which are used in identifying those influential nodes in weighted and weightless networks. TOPSIS is another basic and multi-criteria method which employs four criteria of centrality simultaneously to identify influential nodes; a fact that makes it more accurate than the above criteria. Another method used for identifying influential or top-k influential nodes in complex social networks is Heat Diffusion Kernel: As one of the Topological Diffusion Models; this model identifies nodes based on heat diffusion. In the present paper, to use the topological diffusion model, the social network graph is drawn up by the interactive and non-interactive activities; then, based on the diffusion, the dynamic equations of the graph are modeled. This was followed by using improved heat diffusion kernels to improve the accuracy of influential nodes identification. After several re-administrations of the topological diffusion models, those users who diffused more heat were chosen as the most influential nodes in the concerned social network. Finally, to evaluate the model, the current method was compared with Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS).

Efficient pattern matching for graphs with multi-Labeled nodes

Graph matching is important for a wide variety of applications in different domains such as social network analysis and knowledge discovery. Despite extensive research over the last few decades, graph matching is still challenging particularly when it comes with new conditions and constraints. In this paper, we focus on a new class of graph matching, in which each node can accept multiple labels instead of one. In particular, we address the problem of finding the top-k nodes of a data graph which best match a labeled query node from a given pattern graph. We firstly prove this to be an NP-Complete problem. Then, to address this issue and improve the scalability of our approach, we introduce a more flexible graph simulation, namely surjective simulation. This new graph simulation reduces the unnecessary complexity that is due to the unnecessary constraints imposed by the existing definitions while achieving high-quality matching results. In addition, our approach is associated with an early stop strategy to further boost the performance. To approximate the maximum size of a simulation, our approach utilizes Metropolis Hastings algorithm and ranks the top-k matches after computing the set of surjective simulations. The experimental results over social network graphs demonstrate the efficiency of the proposed approach and superiority over existing approaches.

Maximizing influence under influence loss constraint in social networks

Influence maximization is a fundamental research problem in social networks. Viral marketing, one of its applications, aims to select a small set of users to adopt a product, so that the word-of-mouth effect can subsequently trigger a large cascade of further adoption in social networks. The problem of influence maximization is to select a set of K nodes from a social network so that the spread of influence is maximized over the network. Previous research on mining top-K influential nodes assumes that all of the selected K nodes can propagate the influence as expected. However, some of the selected nodes may not function well in practice, which leads to influence loss of top-K nodes. In this paper, we study an alternative influence maximization problem which is naturally motivated by the reliability constraint of nodes in social networks. We aim to find top-K influential nodes given a threshold of influence loss due to the failure of a subset of R(<K) nodes. To solve the new type of influence maximization problem, we propose an approach based on constrained simulated annealing and further improve its performance through efficiently estimating the influence loss. We provide experimental results over multiple real-world social networks in support. This research will further support practical applications of social networks in various domains particularly where reliability would be a main concern in a system deployment.

Fast Inbound Top-K Query for Random Walk with Restart.

Random walk with restart (RWR) is widely recognized as one of the most important node proximity measures for graphs, as it captures the holistic graph structure and is robust to noise in the graph. In this paper, we study a novel query based on the RWR measure, called the inbound top-k (Ink) query. Given a query node q and a number k, the Ink query aims at retrieving k nodes in the graph that have the largest weighted RWR scores to q. Ink queries can be highly useful for various applications such as traffic scheduling, disease treatment, and targeted advertising. Nevertheless, none of the existing RWR computation techniques can accurately and efficiently process the Ink query in large graphs. We propose two algorithms, namely Squeeze and Ripple, both of which can accurately answer the Ink query in a fast and incremental manner. To identify the top-k nodes, Squeeze iteratively performs matrix-vector multiplication and estimates the lower and upper bounds for all the nodes in the graph. Ripple employs a more aggressive strategy by only estimating the RWR scores for the nodes falling in the vicinity of q, the nodes outside the vicinity do not need to be evaluated because their RWR scores are propagated from the boundary of the vicinity and thus upper bounded. Ripple incrementally expands the vicinity until the top-k result set can be obtained. Our extensive experiments on real-life graph data sets show that Ink queries can retrieve interesting results, and the proposed algorithms are orders of magnitude faster than state-of-the-art method.

Mining top-k influential nodes in social networks via community detection

Influence maximisation is a challenging problem with high computational complexity. It aims to find a small set of seed nodes in a social network that maximises the spread of influence under a certain influence model. In this paper, we propose a community-based greedy algorithm for mining top-k influential nodes in a social network. Our method consists of two separate steps: community detection and top-k nodes mining. In the first step, we use an efficient algorithm to discover the community structure in a network. Then a 'divide and conquer' process is adopted to find the top-k influential nodes from the network. Experimental results on real-world networks show that our method is effective for mining highly influential nodes in networks. Moreover, it is more efficient than the traditional algorithms using greedy policy.

Effective keyword query processing with an extended answer structure in large graph databases

Purpose – The paper aims to propose an effective method to process keyword-based queries over graph-structured databases which are widely used in various applications such as XML, semantic web, and social network services. To satisfy users' information need, it proposes an extended answer structure for keyword queries, inverted list indexes on keywords and nodes, and query processing algorithms exploiting the inverted lists. The study aims to provide more effective and relevant answers to a given query than the previous approaches in an efficient way. Design/methodology/approach – A new relevance measure for nodes to a given keyword query is defined in the paper and according to the relevance metric, a new answer tree structure is proposed which has no constraint on the number of keyword nodes chosen for each query keyword. For efficient query processing, an inverted list-style index is suggested which pre-computes connectivity and relevance information on the nodes in the graph. Then, a query processing algorithm based on the pre-constructed inverted lists is designed, which aggregates list entries for each graph node relevant to given keywords and identifies top-k root nodes of answer trees most relevant to the given query. The basic search method is also enhanced by using extend inverted lists which store additional relevance information of the related entries in the lists in order to estimate the relevance score of a node more closely and to find top-k answers more efficiently. Findings – Experiments with real datasets and various test queries were conducted for evaluating effectiveness and performance of the proposed methods in comparison with one of the previous approaches. The experimental results show that the proposed methods with an extended answer structure produce more effective top-k results than the compared previous method for most of the queries, especially for those with OR semantics. An extended inverted list and enhanced search algorithm are shown to achieve much improvement on the execution performance compared to the basic search method. Originality/value – This paper proposes a new extended answer structure and query processing scheme for keyword queries on graph databases which can satisfy the users' information need represented by a keyword set having various semantics.

Influence maximization algorithm for micro-blog network

Influence maximization problem in micro-blog cannot be solved by simple user rank algorithm.To solve this problem,a greedy algorithm based on Extended Linear Threshold Model(ELTM) was proposed to solve Top-K problem in microblog.A concept of influence rate and a WIR(Weibo Influence Rank) algorithm were established to determine the user's influence by summarizing the key factors.Then,based on WIR values,an influence propagation model was proposed.After using greedy algorithm,the Top-K nodes were excavated.A simulation test based on Sina micro-blog was performed to validate the effectiveness of the proposed method.The result shows that the method outperforms the traditional algorithm.

Fast and Exact Top-k Algorithm for PageRank

To obtain high PageRank score nodes, the original approach iteratively computes the Page-Rank score of each node until convergence by using the whole graph. If the graph is large, this approach is infeasible due to its high computational cost. The goal of this study is to find top-k Page\-Rank score nodes efficiently for a given graph without sacrificing accuracy. Our solution, F-Rank, is based on two ideas: (1) It iteratively estimates lower/upper bounds of Page\-Rank scores, and (2) It constructs subgraphs in each iteration by pruning unnecessary nodes and edges to identify top-k nodes. Our theoretical analysis shows that F-Rank guarantees result exactness. Experiments show that F-Rank finds top-k nodes much faster than the original approach.

An efficient community detection method based on rank centrality

Community detection is a very important problem in social network analysis. Classical clustering approach, K-means, has been shown to be very efficient to detect communities in networks. However, K-means is quite sensitive to the initial centroids or seeds, especially when it is used to detect communities. To solve this problem, in this study, we propose an efficient algorithm K-rank, which selects the top-K nodes with the highest rank centrality as the initial seeds, and updates these seeds by using an iterative technique like K-means. Then we extend K-rank to partition directed, weighted networks, and to detect overlapping communities. The empirical study on synthetic and real networks show that K-rank is robust and better than the state-of-the-art algorithms including K-means, BGLL, LPA, infomap and OSLOM.

Preference-based mining of top-[formula omitted] influential nodes in social networks

Many important applications can be generalized as the influence maximization problem, which targets finding a K-node set in a social network that has the maximum influence. Previous work only considers that influence is propagated through the network with a uniform probability. However, because users actually have different preferences on topics, such a uniform propagation can result in inaccurate results.To solve this problem, we have designed a two-stage mining algorithm (GAUP) to mine the most influential nodes in a network on a given topic. Given a set of users’ documents labeled with topics, GAUP first computes user preferences with a latent feature model based on SVD or a model based on vector space. Then to find top-K nodes in the second stage, GAUP adopts a greedy algorithm that is guaranteed to find a solution within 63% of the optimal. Our evaluation on the task of expert finding shows that GAUP performs better than the state-of-the-art greedy algorithm, SVD-based collaborative filtering, and HITS.

Fast and exact top-k search for random walk with restart

Graphs are fundamental data structures and have been employed for centuries to model real-world systems and phenomena. Random walk with restart (RWR) provides a good proximity score between two nodes in a graph, and it has been successfully used in many applications such as automatic image captioning, recommender systems, and link prediction. The goal of this work is to find nodes that have top-k highest proximities for a given node. Previous approaches to this problem find nodes efficiently at the expense of exactness. The main motivation of this paper is to answer, in the affirmative, the question, 'Is it possible to improve the search time without sacrificing the exactness?'. Our solution, K-dash , is based on two ideas: (1) It computes the proximity of a selected node efficiently by sparse matrices, and (2) It skips unnecessary proximity computations when searching for the top-k nodes. Theoretical analyses show that K-dash guarantees result exactness. We perform comprehensive experiments to verify the efficiency of K-dash. The results show that K-dash can find top-k nodes significantly faster than the previous approaches while it guarantees exactness.