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Register a new userOvercoming Bias in Community Detection Evaluation
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Jeancarlo Campos Le\\~ao
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Community detection is a key task to further understand the function and the structure of complex networks. Therefore, a strategy used to assess this task must be able to avoid biased and incorrect results that might invalidate further analyses or applications that rely on such communities. Two widely used strategies to assess this task are generally known as structural and functional. The structural strategy basically consists in detecting and assessing such communities by using multiple methods and structural metrics. On the other hand, the functional strategy might be used when ground truth data are available to assess the detected communities. However, the evaluation of communities based on such strategies is usually done in experimental configurations that are largely susceptible to biases, a situation that is inherent to algorithms, metrics and network data used in this task. Furthermore, such strategies are not systematically combined in a way that allows for the identification and mitigation of bias in the algorithms, metrics or network data to converge into more consistent results. In this context, the main contribution of this article is an approach that supports a robust quality evaluation when detecting communities in real-world networks. In our approach, we measure the quality of a community by applying the structural and functional strategies, and the combination of both, to obtain different pieces of evidence. Then, we consider the divergences and the consensus among the pieces of evidence to identify and overcome possible sources of bias in community detection algorithms, evaluation metrics, and network data. Experiments conducted with several real and synthetic networks provided results that show the effectiveness of our approach to obtain more consistent conclusions about the quality of the detected communities.
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Community structures are critical towards understanding not only the network topology but also how the network functions. However, how to evaluate the quality of detected community structures is still challenging and remains unsolved. The most widely used metric, normalized mutual information (NMI), was proved to have finite size effect, and its improved form relative normalized mutual information (rNMI) has reverse finite size effect. Corrected normalized mutual information (cNMI) was thus proposed and has neither finite size effect nor reverse finite size effect. However, in this paper we show that cNMI violates the so-called proportionality assumption. In addition, NMI-type metrics have the problem of ignoring importance of small communities. Finally, they cannot be used to evaluate a single community of interest. In this paper, we map the computed community labels to the ground-truth ones through integer linear programming, then use kappa index and F-score to evaluate the detected community structures. Experimental results demonstrate the advantages of our method.
We study the community detection problem on a Gaussian mixture model, in which vertices are divided into $kgeq 2$ distinct communities. The major difference in our model is that the intensities for Gaussian perturbations are different for different entries in the observation matrix, and we do not assume that every community has the same number of vertices. We explicitly find the threshold for the exact recovery of the maximum likelihood estimation. Applications include the community detection on hypergraphs.
We develop a Bayesian hierarchical model to identify communities in networks for which we do not observe the edges directly, but instead observe a series of interdependent signals for each of the nodes. Fitting the model provides an end-to-end community detection algorithm that does not extract information as a sequence of point estimates but propagates uncertainties from the raw data to the community labels. Our approach naturally supports multiscale community detection as well as the selection of an optimal scale using model comparison. We study the properties of the algorithm using synthetic data and apply it to daily returns of constituents of the S&P100 index as well as climate data from US cities.
Analyzing the groups in the network based on same attributes, functions or connections between nodes is a way to understand network information. The task of discovering a series of node groups is called community detection. Generally, two types of information can be utilized to fulfill this task, i.e., the link structures and the node attributes. The temporal text network is a special kind of network that contains both sources of information. Typical representatives include online blog networks, the World Wide Web (WWW) and academic citation networks. In this paper, we study the problem of overlapping community detection in temporal text network. By examining 32 large temporal text networks, we find a lot of edges connecting two nodes with no common community and discover that nodes in the same community share similar textual contents. This scenario cannot be quantitatively modeled by practically all existing community detection methods. Motivated by these empirical observations, we propose MAGIC (Model Affiliation Graph with Interacting Communities), a generative model which captures community interactions and considers the information from both link structures and node attributes. Our experiments on 3 types of datasets show that MAGIC achieves large improvements over 4 state-of-the-art methods in terms of 4 widely-used metrics.
Complex systems, abstractly represented as networks, are ubiquitous in everyday life. Analyzing and understanding these systems requires, among others, tools for community detection. As no single best community detection algorithm can exist, robustness across a wide variety of problem settings is desirable. In this work, we present Synwalk, a random walk-based community detection method. Synwalk builds upon a solid theoretical basis and detects communities by synthesizing the random walk induced by the given network from a class of candidate random walks. We thoroughly validate the effectiveness of our approach on synthetic and empirical networks, respectively, and compare Synwalks performance with the performance of Infomap and Walktrap. Our results indicate that Synwalk performs robustly on networks with varying mixing parameters and degree distributions. We outperform Infomap on networks with high mixing parameter, and Infomap and Walktrap on networks with many small communities and low average degree. Our work has a potential to inspire further development of community detection via synthesis of random walks and we provide concrete ideas for future research.
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