Robust local community detection

HoSIM: Higher-order Structural Importance based method for multiple local community detection

There has been significant interest in the study of the problem of community search in large networks. Given one or more query nodes, this problem aims to discover densely connected subgroups containing these nodes. Various algorithms have been proposed to solve this challenging problem using different measures or a variety of cohesive subgraphs. In this paper, given an undirected graph and a set of query nodes, we study the community search using novel several cohesive subgraph models. More precisely, we propose to exploit several cohesive structures in a unified framework to find densely communities for query nodes in large complex networks. First, we review some existing cohesive structures. Next, to make these structures more effective models of communities, we focus on interesting configurations that are larger and more cohesive by fulfilling some constraints. The new structures obtained allow to ensure a larger density on the discovered communities and overcome some weaknesses of existing models. Finally, empirical results show the effectiveness of our framework to find communities for query nodes in a variety of real graphs.

With the advent of mobile and internet technology, social media has experienced a tremendous growth over the past few years. Social networking site such as Twitter provides the platform for content generation, information dissemination and communications. Summarizing the textual contents or extracting the influential words is a challenging task. Therefore an unsupervised graph based approach is proposed for automatic Keyword Extraction from the collection of Tweets using Node and Edge Weight (KETNEW). The node weight and edge weight depends on various parameters such as tweet frequency, position of node, clustering coefficient, co-occurrence frequency and shared neighbors. The important aspect of our approach is that it is a hybrid approach i.e. a graph based approach (structural approach) which depends upon statistical as well as linguistic features. Apart from this, it also depends on the position of the word in the text. These numerous features made it perform better than existing techniques.

Software fault localization technology aims to identify suspicious statements that cause software failures, which is crucial for ensuring software quality. Spectrum-based software fault location (SBFL) technology calculates the suspiciousness of each statement by analyzing the correlation between statement coverage information and execution results in test cases. SBFL has attracted increasing attention from scholars due to its high efficiency and scalability. However, existing SBFL studies have shown that a large number of statements share the same suspiciousness, which hinders software debuggers from quickly identifying the location of faulty statements. To address this challenge, we propose an SBFL model based on weighted association rule mining and complex networks: FL-WARMCN. The algorithm first uses Jaccard to measure the distance between passing and failing test cases, and applies it as the weight of passing test cases. Next, FL-WARMCN calculates the initial suspiciousness of each statement based on the program spectrum data. Then, the FL-WARMCN model utilizes a weighted association rule mining algorithm to obtain the correlation relationships between statements and models the network based on this. In the network, the suspiciousness of statements is used as node weights, and the correlation between statements is used as edge weights. We chose the eigenvector centrality that takes into account the degree centrality of statements and the importance of neighboring statements to calculate the importance of each statement, and used it as a weight to incorporate into the weighted suspiciousness calculation of the statement. Finally, we applied the FL-WARMCN model for experimental validation on the Defects4J dataset. The results showed that the model was significantly superior to other baselines. In addition, we analyzed the impact of different node and edge weights on model performance.

In many domains it is necessary to generate surrogate networks, e.g., for hypothesis testing of different properties of a network. Generating surrogate networks typically requires that different properties of the network are preserved, e.g., edges may not be added or deleted and edge weights may be restricted to certain intervals. In this paper we present an efficient property-preserving Markov chain Monte Carlo method termed CycleSampler for generating surrogate networks in which (1) edge weights are constrained to intervals and vertex strengths are preserved exactly, and (2) edge and vertex strengths are both constrained to intervals. These two types of constraints cover a wide variety of practical use cases. The method is applicable to both undirected and directed graphs. We empirically demonstrate the efficiency of the CycleSampler method on real-world data sets. We provide an implementation of CycleSampler in R, with parts implemented in C.

When multiple agents have an opportunity to invest in improving the common good, the free rider problem arises. Many businesses and policymakers grapple with these free rider problems arising from the common good. For instance, this problem arises when governments or individual firms decide how much to invest in the environment as the common good. The theoretical examination of the mathematical model in previous literature has shown a mild form of the free rider effect. In stark contrast, the article, “Game of Variable Contributions to the Common Good under Uncertainty,” shows that a simple game-theoretic model of investment in the common good can result in a severe form of the free rider effect, much akin to what happens in a game of chicken. The article also shows that if the players are asymmetric and the environment has randomness, the free rider effect can be mitigated.

Even though Peer-to-Peer (P2P) systems present a cost-effective and scalable solution to content distribution, most entertainment, media and software, content providers continue to rely on expensive, centralized solutions such as Content Delivery Networks. One of the main reasons is that the current P2P systems cannot guarantee reasonable performance as they depend on the willingness of users to contribute bandwidth. Moreover, even systems like BitTorrent, which employ a tit-for-tat protocol to encourage fair bandwidth exchange between users, are prone to free-riding (i.e. peers that do not upload). Our experiments on PlanetLab extend previous research [14, 12, 11] demonstrating that such selfish behavior can seriously degrade the performance of regular users in many more scenarios beyond simple free-riding: we observed an overhead of upto 430% for 80% of free-riding identities easily generated by a small set of selfish users. To mitigate the effects of selfish users, we propose a new P2P architecture that classifies peers with the help of a small number of trusted nodes that we call Trusted Auditors (TAs). TAs participate in P2P download like regular clients and detect free-riding identities by observing their neighbors’ behavior. Using TAs, we can separate compliant users into a separate service pool resulting in better performance. Furthermore, we show that TAs are more effective ensuring the performance of the system than a mere increase in bandwidth capacity: for 80% of freeriding identities a single-TA system has a 6% download time overhead while without the TA and three times the bandwidth capacity we measure a 100% overhead.

The information capture of spatial node is the main application of mobile relaying Mesh Network. Due to the insufficiency of existing spatial query algorithm IWQE and EIWQE, this paper proposes an optimized spatial query algorithm EI2WQE based on neighbor transfer and redundancy recovery. It utilizes the broadcasting nature of wireless signal to receive neighbor node of the broadcasting signal. As redundancy backups for potential failure and ensures that the query route can be recovered in case of a query interruption. Detailed improvements include: (1) Balanced processing for number of neighbors at query node. For processing balance, EI2WQE algorithm transfers the query node whose neighbors are above average to the query node with fewer neighbors. (2) Reliability of query route. This paper describes the detailed process of EI2WQE, and verifies the advantage of EI2WQE in terms of query success rate and node energy consumption balance by simulation on OMNET++ Platform.

Community detection task aims to find communities for a given network while community search focuses on finding a densely connected community with some given query nodes. Community search methods have the advantage of speed as they find a community for a set of query nodes instead of finding all the communities. They also have better performance on handling dynamic networks compared to community detection algorithms. However, current studies of community search are not well designed to handle spatial sensitive networks. We propose a new community search method, Spatial-based Search using PageRank Vector (SSPPV), which aims to find a community for a given query node over a large spatial social network. SSPPV finds a community with not only good cluster quality but also a smaller spatial size of the resulting community, compared to existing methods.

It is common in the study of networks to investigate intermediate-sized (or "meso-scale") features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify "communities," which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that communities are associated with bottlenecks of locally biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for "size-resolved community structure" that can arise in real (and realistic) networks: (1) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (2) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (3) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions. Depending on which scenario holds, one may or may not be able to successfully identify "good" communities in a given network (and good communities might not even exist for a given community quality measure), the manner in which different small communities fit together to form meso-scale network structures can be very different, and processes such as viral propagation and information diffusion can exhibit very different dynamics. In addition, our results suggest that, for many large realistic networks, the output of locally biased methods that focus on communities that are centered around a given seed node (or set of seed nodes) might have better conceptual grounding and greater practical utility than the output of global community-detection methods. They also illustrate structural properties that are important to consider in the development of better benchmark networks to test methods for community detection.

A community in a network is a set of nodes that are densely and closely connected within the set, yet sparsely connected to nodes outside of it. Detecting communities in large networks helps solve many real-world problems. However, detecting such communities in a complex network by focusing on the whole network is costly. Instead, one can focus on finding overlapping communities starting from one or more seed nodes of interest. Moreover, on the online setting the network is given as a stream of higher order structures, i.e., triangles of nodes to be clustered into communities.In this paper, we propose an on online local graph community detection algorithm that uses motifs, such as triangles of nodes. We provide experimental results and compare it to another algorithm named COEUS. We use two public datasets, one of Amazon data and the other of DBLP data. Furthermore, we create and experiment on a new dataset that consists of web pages and their links by using the Internet Archive. This latter dataset provides insights to better understand how working with motifs is different than working with edges.

In this paper, three new algorithms, a greedy algorithm, a KL-like algorithm, and an add-all algorithm, are proposed to find local optimal community structures in large networks starting from a given source vertex. The time complexity for finding a local community of all these algorithms is O(K 2d), where K is the number of vertices to be explored in the sub-graph and d is the average degree of the vertices in the sub-graph. A JAVA tool is developed based on these algorithms to identify local community structures in large networks. The results of using this tool to analyze a co-purchase network from Amazon.com show that local community structures exist in this large-scale co-purchase network. Further analyses of the identified local communities show that purchases of media items form more compact local communities than purchases of book items do, indicating that recommending digital media items to customers based on co-purchasing information in the online store is more efficient than recommending books.

Analysts’ strategic inducement of management guidance

Weighted signed networks (WSNs) are networks in which edges are labeled with positive and negative weights. WSNs can capture like/dislike, trust/distrust, and other social relationships between people. In this paper, we consider the problem of predicting the weights of edges in such networks. We propose two novel measures of node behavior: the goodness of a node intuitively captures how much this node is liked/trusted by other nodes, while the fairness of a node captures how fair the node is in rating other nodes' likeability or trust level. We provide axioms that these two notions need to satisfy and show that past work does not meet these requirements for WSNs. We provide a mutually recursive definition of these two concepts and prove that they converge to a unique solution in linear time. We use the two measures to predict the edge weight in WSNs. Furthermore, we show that when compared against several individual algorithms from both the signed and unsigned social network literature, our fairness and goodness metrics almost always have the best predictive power. We then use these as features in different multiple regression models and show that we can predict edge weights on 2 Bitcoin WSNs, an Epinions WSN, 2 WSNs derived from Wikipedia, and a WSN derived from Twitter with more accurate results than past work. Moreover, fairness and goodness metrics form the most significant feature for prediction in most (but not all) cases.

Searching for local communities is an important research challenge that allows for personalized community discovery and supports advanced data analysis in various complex networks, such as the World Wide Web, social networks, and brain networks. The evolution of these networks over time has motivated several recent studies to identify local communities in temporal networks. Given any query nodes, Community Search aims to find a densely connected subgraph containing query nodes. However, existing community search approaches in temporal networks have two main limitations: (1) they adopt pre-defined subgraph patterns to model communities, which cannot find communities that do not conform to these patterns in real-world networks, and (2) they only use the aggregation of disjoint structural information to measure quality, missing the dynamic of connections and temporal properties. In this paper, we propose a query-driven Temporal Graph Convolutional Network (CS-TGN) that can capture flexible community structures by learning from the ground-truth communities in a data-driven manner. CS-TGN first combines the local query-dependent structure and the global graph embedding in each snapshot of the network and then uses a GRU cell with contextual attention to learn the dynamics of interactions and update node embeddings over time. We demonstrate how this model can be used for interactive community search in an online setting, allowing users to evaluate the found communities and provide feedback. Experiments on real-world temporal graphs with ground-truth communities validate the superior quality of the solutions obtained and the efficiency of our model in both temporal and interactive static settings.