Exploring the roles of cannot-link constraint in community detection via Multi-variance Mixed Gaussian Generative Model.

The research on community structure is a key to analyze the network functionality and topology, and thus it is significant to detect and analysis the community structure. During the abstract process from an actual system to a network, especially for a large-scale network, it is inevitable to have mistaken connections between nodes or have connection missing. In addition, in real applications, from time to time we can obtain prior information in the form of pairwise constraints between nodes besides topology information, although they may be inaccurate or conflicted. These noises in the network-related information will dramatically reduce the accuracy of community detection. Hence, in this paper, we introduce a dissimilarity index to determine the trustworthiness of pairwise constraints and settle the conflict of pairwise constraints. Then, focusing on the community detection with false connections or conflicted connections, we propose a pairwise constrained structure-enhanced extremal optimization-based semi-supervised algorithm (PCSEO-SS algorithm). Compared with existing semi-supervised community detection approaches, the experimental results executed on real networks and synthetic networks, show that PCSEO-SS can solve the problem of false connections or conflicted connections to some extent and detect the community structure more precisely.

Community detection methods that are based entirely on the topology of the network do not always achieve higher accuracy. This implies that the topological information alone is insufficient to accurately uncover the community structures of networks. Recently, some methods were proposed that used prior information to improve the performance and accuracy of community detection. However, most of these methods have high time consumption and are not suitable for dealing with large-scale networks. In this paper, we propose a fast semi-supervised community detection method called SemiAttractor that integrates the prior information into the distance dynamics model. Experimental results from both artificial and real-world networks show that the proposed method can effectively improve the accuracy of community detection and reduce the time costs.

Adaptive semi-supervised dimensionality reduction with sparse representation using pairwise constraints

In this paper, we consider a general problem of multiple view learning from pairwise constraints in the form of must-link and cannot-link. As one kind of side information, the must-link constraints imply that a pair of instances belongs to the same class, while the cannot-link constraints compel them to be different classes. Given must-link and cannot-link constraints, we propose a new locality preserving projections approach for multiple view semi-supervised dimensionality reduction. The goal of the proposed approach uses pairwise constraints to derive embedding in each view together with the unlabeled instances. Hence, the consensus pattern can be learned from multiple embeddings of multiple representations. Experimental results on real-world data sets show the effectiveness of the proposed algorithm.

Improving clustering with constrained communities

While in standard clustering no side information is used, users might be interested in providing additional information to influence the clustering. In case of document clustering, additional information can take the form of pairwise constraints where a user provides additional information about pairs of documents as must-link and cannot-link constraints (indicating respectively whether the documents in the pair are coming from the same cluster or not). In this paper, we propose a novel deep document clustering framework which can employ pairwise constraints while learning document representations to obtain better tailored results. Indeed, in our proposed framework, data representations (obtained through an autoencoder) and cluster representatives are learned through back propagation in a joint way. Devising a fully differentiable deep clustering framework with the ability of using pairwise constraints is the main contribution of this paper. Experiments conducted on 5 public datasets show the gain in clustering performance which the resulting approach can yield.

Community detection is an effective approach to unveil relationships among individuals in online social networks. In the literature, quite a few algorithms have been proposed to conduct community detection by exploiting the topology of social networks and the attributes of social actors. In practice, community detection is usually conducted by third parties, such as advertisement companies and hospitals, with access to social networks for different purposes, which can easily lead to a privacy breach. In this paper, we investigate community detection in social networks aiming to protect the privacy of both the network topology and the users' attributes. We show that with additional prior knowledge, community detection can be performed by querying the information of only a fraction of instead of the entire population. In particular, we first propose a new scheme called differentially private community detection (DPCD). DPCD detects communities in social networks via a probabilistic generative model, which can be decomposed into subproblems solved by individual users. The private social relationships and attributes of each user are protected by objective perturbation with differential privacy guarantees. Then, we propose a parsimonious node affiliation recovery (NAR) algorithm, which is also differentially private, to unveil the community affiliation information of the whole population based on that of the limited number of queried individuals by solving a sparse optimization problem. Through both theoretical analysis and experimental validation using synthetic and real-world social networks, we demonstrate that the proposed DPCD scheme detects social communities under the modest privacy budget. In addition, we show the effectiveness of NAR to perform community detection by querying a limited number of individuals in social networks.

In semisupervised community detection, the membership of a set of revealed nodes is known in addition to the graph structure and can be leveraged to achieve better inference accuracies. While previous works investigated the case where the revealed nodes are selected at random, this paper focuses on correlated subsets leading to atypically high accuracies. In the framework of the dense stochastic block model, we employ statistical physics methods to derive a large deviation analysis of the number of these rare subsets, as characterized by their free energy. We find theoretical evidence of a nonmonotonic relationship between reconstruction accuracy and the free energy associated to the posterior measure of the inference problem. We further discuss possible implications for active learning applications in community detection.

Feature selection is an important pre-processing stage in many machine learning and pattern recognition tasks, which eliminates irrelevant and redundant features and improves learning performance. Regularized sparse feature selection methods like Lasso and its variants using l 1 -norm regularization term in their optimization problem have received much attention in recent years. Prior information could be represented as the class labels or pairwise constraints, i.e., must-link (positive) and cannot-link (negative) constraints. In this paper, besides the l 1 -norm regularization term, a normalized adapted affinity matrix is applied to embed the pairwise constraints in the affinity matrix. In the proposed affinity matrix, the weights are strengthened/weakened according to the positive/negative constraints. The experimental results on several data sets from University of California-Irvine (UCI) machine learning repository and a high dimensional data set, show the effectiveness of the proposed method in the classification tasks compared to some similar feature selection methods.

This paper <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> focuses on spectrum allocation and medium access in future cognitive radio wireless networks, e.g. IEEE 802.22 networks. For a given network topology, the proposed spectrum allocation method first generates an interference graph (or conflict graph) based on the network topology, traffic flows and interference constraints. Then a graph coloring algorithm is applied to the interference graph and the chromatic number of the graph is obtained. This number is the minimum number of distinct spectrum slices required. Further, spectrum allocation is done based on the max-min fair share algorithm or proportional fair algorithm according to the spectrum demands for different spectrum blocks. For medium access among secondary users, we then describe and implement a MAC protocol for 802.22-type cognitive radio networks. Simulation results are presented to demonstrate its effectiveness.

Detecting community structures is an important research topic in social network analysis. Unfortunately, the fundamental factors that drive the generation of social networks (i.e., the network topology and content) and community structures have not been well investigated. In this paper, according to the natural characteristics of social networks, we reveal that individual topics play a core role in community generation. If two individuals are in the same community and are interested in similar topics, it is more likely that a link will form between them. Otherwise, the probability of generating a link depends on the relationships between their communities and the topics they talk about. Based on the above observations, a novel generative community detection model is proposed that simulates the generation of the network topology and network content by considering individual topics. Moreover, our model utilizes a topic model to generate network content. The model is evaluated on two real-world datasets. The experimental results show that the community detection results outperform all the state-of-the-art baselines. In addition to accurate community detection results, we identify each individual topic distribution and the most popular users corresponding to different topics in each community.

Community detection is one of the popular research topics in the field of complex networks analysis. It aims to identify communities, represented as cohesive subgroups or clusters, where nodes in the same community link to each other more densely than others outside. Due to the interpretability, simplicity, flexibility, and generality, nonnegative matrix factorization (NMF) has become a very ideal model for community detection and lots of related methods have been presented. To facilitate research on NMF-based community detection, in this article, we make a comprehensive review on NMF-based methods for community detection, especially the state-of-the-art methods presented in high prestige journals or conferences. First, we introduce the basic principles of NMF and explain why NMF can detect communities and design a general framework of NMF-based community detection. Second, according to the applicable network types, we propose a taxonomy to divide the existing NMF-based methods for community detection into six categories, namely, topology networks, signed networks, attributed networks, multilayer networks, dynamic networks, and large-scale networks. We deeply analyze representative methods in every category. Finally, we summarize the common problems faced by all methods and potential solutions and propose four promising research directions. We believe that this survey can fully demonstrate the versatility of NMF-based community detection and serve as a useful guideline for researchers in related fields.

The realization of the Smart Grid vision will change the way of producing and distributing electrical energy. It paves the road for end-users to become pro-active in the distribution system and, equipped with renewable energy generators such as a photovoltaic panel, to become a so called “prosumer”. The prosumer is engaged in both energy production and consumption. Prosumers’ energy can be transmitted and exchanged as a commodity between end-users, disrupting the traditional utility model. The appeal of such scenario lies in the engagement of the end user, in facilitating the introduction and optimization of renewables, and in engaging the end-user in its energy management. To facilitate the transition to a prosumers’ governed grid, we propose a novel strategy for optimizing decentralized energy exchange in digitalized power grids, i.e., the Smart Grid. The strategy considers prosumer’s involvement, energy loss of delivery, network topology, and physical constraints of distribution networks. To evaluate the solution, we build a simulation program and design three meaningful evaluation cases according to different energy flow patterns. The simulation results indicate that, compared to traditional power distribution system, the maximum reduction of energy loss, energy costs, energy provided by the electric utility based using the proposed strategy can reach 51 % , 66 % , 97.5 % , depending on the strategy. Moreover, the proportion of energy self-satisfaction approaches reaches 98 % .

Community detection in complex networks using edge-deleting with restrictions

The problem of graph clustering or community detection has enjoyed a lot of attention in complex networks literature. A quality function, modularity, quantifies the strength of clustering and on maximization yields sensible partitions. However, in most real world networks, there are an exponentially large number of near-optimal partitions with some being very different from each other. Therefore, picking an optimal clustering among the alternatives does not provide complete information about network topology. To tackle this problem, we propose a graph perturbation scheme which can be used to identify an ensemble of near-optimal and diverse clusterings. We establish analytical properties of modularity function under the perturbation which ensures diversity. Our approach is algorithm independent and therefore can leverage any of the existing modularity maximizing algorithms. We numerically show that our methodology can systematically identify very different partitions on several existing data sets. The knowledge of diverse partitions sheds more light into the topological organization and helps gain a more complete understanding of the underlying complex network.