Query: Semi-supervised clustering with metric learning: An adaptive kernel method

Relevant passages:
Constrained K-means Clustering with Background Knowledge <=SEP=> Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular k-means clustering algorithm can be profitably modified to make use of this information. In experiments with artificial constraints on six data sets, we observe improvements in clustering accuracy. We also apply this method to the real-world problem of automatically detecting road lanes from GPS data and observe dramatic increases in performance.

Integrating constraints and metric learning in semi-supervised clustering <=SEP=> Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning. Previous work in the area has utilized supervised data in one of two approaches: 1) constraint-based methods that guide the clustering algorithm towards a better grouping of the data, and 2) distance-function learning methods that adapt the underlying similarity metric used by the clustering algorithm. This paper provides new methods for the two approaches as well as presents a new semi-supervised clustering algorithm that integrates both of these techniques in a uniform, principled framework. Experimental results demonstrate that the unified approach produces better clusters than both individual approaches as well as previously proposed semi-supervised clustering algorithms.

Distance Metric Learning with Application to Clustering with Side-Information <=SEP=> Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many “plausible” ways, and if a clustering algorithm such as K-means initially fails to find one that is meaningful to a user, the only recourse may be for the user to manually tweak the metric until sufficiently good clusters are found. For these and other applications requiring good metrics, it is desirable that we provide a more systematic way for users to indicate what they consider “similar.” For instance, we may ask them to provide examples. In this paper, we present an algorithm that, given examples of similar (and, if desired, dissimilar) pairs of points in , learns a distance metric over that respects these relationships. Our method is based on posing metric learning as a convex optimization problem, which allows us to give efficient, local-optima-free algorithms. We also demonstrate empirically that the learned metrics can be used to significantly improve clustering performance.

Semi-supervised Clustering by Seeding <=SEP=> 

Learning with kernels <=SEP=> All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.

Introduction: attachment theory and psychotherapy. <=SEP=> In this introduction to the JCLP: In Session 69(11) issue on attachment theory and psychotherapy, the key points of attachment theory (Bowlby, , , 1981) and its relevance to psychotherapy are briefly described. The aim of this issue is to provide case illustrations of how an attachment theory perspective and principles can expand our understanding of psychotherapy practice.

Neural Networks - A Systematic Introduction <=SEP=> In what case do you like reading so much? What about the type of the neural networks a systematic introduction book? The needs to read? Well, everybody has their own reason why should read some books. Mostly, it will relate to their necessity to get knowledge from the book and want to read just to get entertainment. Novels, story book, and other entertaining books become so popular this day. Besides, the scientific books will also be the best reason to choose, especially for the students, teachers, doctors, businessman, and other professions who are fond of reading.

Constrained Deep Metric Learning for Person Re-identification <=SEP=> Person re-identification aims to re-identify the probe image from a given set of images under different camera views. It is challenging due to large variations of pose, illumination, occlusion and camera view. Since the convolutional neural networks (CNN) have excellent capability of feature extraction, certain deep learning methods have been recently applied in person re-identification. However, in person re-identification, the deep networks often suffer from the over-fitting problem. In this paper, we propose a novel CNN-based method to learn a discriminative metric with good robustness to the over-fitting problem in person re-identification. Firstly, a novel deep architecture is built where the Mahalanobis metric is learned with a weight constraint. This weight constraint is used to regularize the learning, so that the learned metric has a better generalization ability. Secondly, we find that the selection of intraclass sample pairs is crucial for learning but has received little attention. To cope with the large intra-class variations in pedestrian images, we propose a novel training strategy named moderate positive mining to prevent the training process from over-fitting to the extreme samples in intraclass pairs. Experiments show that our approach significantly outperforms state-of-the-art methods on several benchmarks of person re-identification.

SQL-to-NoSQL Schema Denormalization and Migration: A Study on Content Management Systems <=SEP=> Content management systems (CMSs) are able to let people, who have no technical skill to manage websites, rapidly create, edit, organize and publish online contents. For example, CMSs are often used to establish e-commerce online shops, community portals, personal blogs, or organization websites. Thus, CMSs play the role of collecting data and then can be easily extended to become intelligent Internet systems. Most popular CMSs, e.g., Word Press and Joomla, rely on relational databases or SQL databases, e.g., MySQL, PostgreSQL, etc. However, due to the explosive growth of huge data, the well-structured characteristic of SQL databases may limit the scalability to handle horizontal scaling. Therefore, regarding the flexibility and the feasibility of parallel processing, cloud computing takes Not Only SQL (NoSQL) databases into consideration. How to migrate the original CMS software from SQL databases to NoSQL databases becomes one emerging and critical research issue. This paper is motivated to propose an autonomous SQL-to-NoSQL schema denormalization and migration. At the same time, this paper also evaluates the proposed mechanism on the realistic CMS software. Based on our experimental results, our mechanism not only helps the current CMS software migrate into the cloud platform without re-designing their database schemas, but also improves at least 45% access performance after schema migration.

Testing Unconstrained Optimization Software <=SEP=> Much of the testing of optimization software is inadequate because the number of test functmns is small or the starting points are close to the solution. In addition, there has been too much emphasm on measurmg the efficmncy of the software and not enough on testing reliability and robustness. To address this need, we have produced a relatwely large but easy-to-use collection of test functions and designed gmdelines for testing the reliability and robustness of unconstrained optimization software.

Short Text Similarity with Word Embeddings <=SEP=> Determining semantic similarity between texts is important in many tasks in information retrieval such as search, query suggestion, automatic summarization and image finding. Many approaches have been suggested, based on lexical matching, handcrafted patterns, syntactic parse trees, external sources of structured semantic knowledge and distributional semantics. However, lexical features, like string matching, do not capture semantic similarity beyond a trivial level. Furthermore, handcrafted patterns and external sources of structured semantic knowledge cannot be assumed to be available in all circumstances and for all domains. Lastly, approaches depending on parse trees are restricted to syntactically well-formed texts, typically of one sentence in length.
 We investigate whether determining short text similarity is possible using only semantic features---where by semantic we mean, pertaining to a representation of meaning---rather than relying on similarity in lexical or syntactic representations. We use word embeddings, vector representations of terms, computed from unlabelled data, that represent terms in a semantic space in which proximity of vectors can be interpreted as semantic similarity.
 We propose to go from word-level to text-level semantics by combining insights from methods based on external sources of semantic knowledge with word embeddings. A novel feature of our approach is that an arbitrary number of word embedding sets can be incorporated. We derive multiple types of meta-features from the comparison of the word vectors for short text pairs, and from the vector means of their respective word embeddings. The features representing labelled short text pairs are used to train a supervised learning algorithm. We use the trained model at testing time to predict the semantic similarity of new, unlabelled pairs of short texts 
 We show on a publicly available evaluation set commonly used for the task of semantic similarity that our method outperforms baseline methods that work under the same conditions.

Securing vulnerable home IoT devices with an in-hub security manager <=SEP=> The proliferation of consumer Internet of Things (IoT) devices offers as many convenient benefits as it poses significant vulnerabilities. Patching or otherwise mitigating these vulnerabilities will be difficult for the existing home security ecosystem. This paper proposes a central security manager that is built on top of the smarthome's hub or gateway router and positioned to intercept all traffic to and from devices. Aware of the status of all devices in the home and of reported vulnerabilities, the security manager could intervene as needed to deter or alleviate many types of security risks. Modules built atop this manager could offer convenient installation of software updates, filter traffic that might otherwise exploit devices, and strengthen authentication for both legacy and future devices. We believe that this design offers the potential to increase security for smarthome IoT devices, and we encourage other researchers and regulators to explore and extend our ideas.

Embedded image coding using zerotrees of wavelet coefficients <=SEP=> The embedded zerotree wavelet algorithm (EZW) is a simple, yet remarkably effective, image compression algorithm, having the property that the bits in the bit stream are generated in order of importance, yielding a fully embedded code. The embedded code represents a sequence of binary decisions that distinguish a n image from the “null” image. Using a n embedded coding algorithm, a n encoder can terminate the encoding a t any point thereby allowing a target rate or target distortion metric to be met exactly. Also, given a bit stream, the decoder can cease decoding a t any point in the bit stream and still produce exactly the same image that would have been encoded a t the bit rate corresponding to the truncated bit stream. In addition to producing a fully embedded bit stream, EZW consistently produces compression results that a re competitive with virtually all known compression algorithms on standard test images. Yet this performance is achieved with a technique that requires absolutely no training, no pre-stored tables or codebooks, and requires no prior knowledge of the image source. The EZW algorithm is based on four key concepts: 1) a discrete wavelet transform or hierarchical subband decomposition, 2) prediction of the absence of significant information across scales by exploiting the self-similarity inherent in images, 3) entropy-coded successive-approximation quantization, and 4) universal lossless data compression which is achieved via adaptive arithmetic coding.

Nutritional Ecology and Human Health. <=SEP=> In contrast to the spectacular advances in the first half of the twentieth century with micronutrient-related diseases, human nutrition science has failed to stem the more recent rise of obesity and associated cardiometabolic disease (OACD). This failure has triggered debate on the problems and limitations of the field and what change is needed to address these. We briefly review the two broad historical phases of human nutrition science and then provide an overview of the main problems that have been implicated in the poor progress of the field with solving OACD. We next introduce the field of nutritional ecology and show how its ecological-evolutionary foundations can enrich human nutrition science by providing the theory to help address its limitations. We end by introducing a modeling approach from nutritional ecology, termed nutritional geometry, and demonstrate how it can help to implement ecological and evolutionary theory in human nutrition to provide new direction and to better understand and manage OACD.

Innovative Cost Effective Approach for Cell Phone Based Remote Controlled Embedded System for Irrigation <=SEP=> This work describes the development of innovative low cost cell phone based remote control application for induction motor-pump based irrigation in agriculture. Rural areas in many states of India are plagued by frequent power cuts and abnormal voltage conditions. The developed system ensures that water is distributed to field whenever normal conditions exist based on task specified. A novel concept of miscall for specified duration has been used to reduce the operational cost of the system and for the convenience of farmers facing difficulty in typing messages. Information is exchanged in form of miscalls / message between the system and the user cell phones. The system is based on AVR ATMega32 micro-controller and includes protection against single phasing, over-current, dry running and other desirable features. DS1307 and DS18S20 are used for time and temperature measurement respectively. It is expected that system will relieve hardships of farmers relating water distribution to a great extent.

Predicting Domain-Specific Risk Taking With the HEXACO Personality Structure <=SEP=> Although risk taking traditionally has been viewed as a unitary, stable individual difference variable, emerging evidence in behavioral decision-making research suggests that risk taking is a domain-specific construct. Utilizing a psychological risk-return framework that regresses risk taking on the perceived benefits and perceived riskiness of an activity (Weber & Milliman, 1997), this study examined the relations between risk attitude and broad personality dimensions using the new HEXACO personality framework (Lee &Ashton, 2004) across four risk domains. This personality framework, which has been replicated in lexical studies in over 12 natural languages, assess personality over six broad personality dimensions, as opposed to the traditional Five-Factor Model, or ‘‘Big Five.’’ Through path analysis, we regressed risk taking in four separate domains on risk perceptions, perceived benefits, and the six HEXACO dimensions. Across all risk domains, we found that the emotionality dimension was associated with heightened risk perceptions and high conscientiousness was associated with less perceived benefits. We also report several unique patterns of domain-specific relations between the HEXACO dimensions and risk attitude. Specifically, openness was associated with risk taking and perceived benefits for social and recreational risks, whereas lower honesty/humility was associated with greater health/safety and ethical risk taking. These findings extend our understanding of how individuals approach risk across a variety of contexts, and further highlight the utility of honesty/humility, a dimension not recovered in Big Five models, in individual differences research. Copyright # 2010 John Wiley & Sons, Ltd. key words risk taking; risk perception; risk-return framework; personality; HEXACO; honesty/humility Risk taking has traditionally been viewed as an enduring, stable, and domain-invariant construct in both behavioral decision making and personality research (e.g., Eysenck & Eysenck, 1977; Kahneman & Tversky, 1979; Paunonen & Jackson, 1996; Tellegen, 1985). However, recent advances suggest that risk taking is content, or domain, specific (Blais &Weber, 2006; Hanoch, Johnson, &Wilke, 2006; Soane & Chmiel, 2005; Weber, Blais, & Betz, 2002). In light of this knowledge, psychologists would benefit from a deeper ler, Decision Research, Eugene, OR 97401, USA. E-mail: jweller@decisionresearch.org

Robust Interactive Image Segmentation Using Convex Active Contours <=SEP=> The state-of-the-art interactive image segmentation algorithms are sensitive to the user inputs and often unable to produce an accurate boundary with a small amount of user interaction. They frequently rely on laborious user editing to refine the segmentation boundary. In this paper, we propose a robust and accurate interactive method based on the recently developed continuous-domain convex active contour model. The proposed method exhibits many desirable properties of an effective interactive image segmentation algorithm, including robustness to user inputs and different initializations, the ability to produce a smooth and accurate boundary contour, and the ability to handle topology changes. Experimental results on a benchmark data set show that the proposed tool is highly effective and outperforms the state-of-the-art interactive image segmentation algorithms.

Model Reference Adaptive Control-Based Speed Control of Brushless DC Motors With Low-Resolution Hall-Effect Sensors <=SEP=> A control system with a novel speed estimation approach based on model reference adaptive control (MRAC) is presented for low cost brushless dc motor drives with low-resolution hall sensors. The back EMF is usually used to estimate speed. But the estimation result is not accurate enough at low speeds because of the divided voltage of stator resistors and too small back EMF. Moreover, the stator resistor is always varying with the motor's temperature. A speed estimation algorithm based on MRAC was proposed to correct the speed error estimated by using back EMF. The proposed algorithm's most innovative feature is its adaptability to the entire speed range including low speeds and high speeds and temperature and different motors do not affect the accuracy of the estimation result. The effectiveness of the algorithm was verified through simulations and experiments.

A Survey on Learning to Hash <=SEP=> Nearest neighbor search is a problem of finding the data points from the database such that the distances from them to the query point are the smallest. Learning to hash is one of the major solutions to this problem and has been widely studied recently. In this paper, we present a comprehensive survey of the learning to hash algorithms, categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise similarity preserving, implicit similarity preserving, as well as quantization, and discuss their relations. We separate quantization from pairwise similarity preserving as the objective function is very different though quantization, as we show, can be derived from preserving the pairwise similarities. In addition, we present the evaluation protocols, and the general performance analysis, and point out that the quantization algorithms perform superiorly in terms of search accuracy, search time cost, and space cost. Finally, we introduce a few emerging topics.

KBQA: An Online Template Based Question Answering System over Freebase <=SEP=> Question answering (QA) has become a popular way for humans to access billion-scale knowledge bases. QA systems over knowledge bases produce accurate and concise answers. The key of QA over knowledge bases is to map the question to a certain substructure in the knowledge base. To do this, KBQA (Question Answering over Knowledge Bases) uses a new kind of question representation: templates, learned from a million scale QA corpora. For example, for questions about a city’s population, KBQA learns templates such as What’s the population of $city?, How many people are there in $city?. It learns overall 1171303 templates for 4690 relations. Based on these templates, KBQA effectively and efficiently supports binary factoid questions or complex questions.

1 Mobile Landscapes : using location data from cellphones for urban analysis <=SEP=> The technology for determining the geographic location of cell phones and other hand-held devices is becoming increasingly available. It is opening the way to a wide range of applications, collectively referred to as Location Based Services (LBS), that are primarily aimed at individual users. However, if deployed to retrieve aggregated data in cities, LBS could become a powerful tool for urban analysis. This paper aims to review and introduce the potential of this technology to the urban planning community. In addition, it presents the ‘Mobile Landscapes’ project: an application in the metropolitan area of Milan, Italy, based on the geographical mapping of cell phone usage at different times of the day. The results enable a graphic representation of the intensity of urban activities and their evolution through space and time. Finally, a number of future applications are discussed and their potential for urban studies and planning is assessed.

Effects of gratitude meditation on neural network functional connectivity and brain-heart coupling <=SEP=> A sense of gratitude is a powerful and positive experience that can promote a happier life, whereas resentment is associated with life dissatisfaction. To explore the effects of gratitude and resentment on mental well-being, we acquired functional magnetic resonance imaging and heart rate (HR) data before, during, and after the gratitude and resentment interventions. Functional connectivity (FC) analysis was conducted to identify the modulatory effects of gratitude on the default mode, emotion, and reward-motivation networks. The average HR was significantly lower during the gratitude intervention than during the resentment intervention. Temporostriatal FC showed a positive correlation with HR during the gratitude intervention, but not during the resentment intervention. Temporostriatal resting-state FC was significantly decreased after the gratitude intervention compared to the resentment intervention. After the gratitude intervention, resting-state FC of the amygdala with the right dorsomedial prefrontal cortex and left dorsal anterior cingulate cortex were positively correlated with anxiety scale and depression scale, respectively. Taken together, our findings shed light on the effect of gratitude meditation on an individual’s mental well-being, and indicate that it may be a means of improving both emotion regulation and self-motivation by modulating resting-state FC in emotion and motivation-related brain regions.

A Research on Iron Loss of IPMSM With a Fractional Number of Slot Per Pole <=SEP=> In this paper, we investigated rotor iron loss of interior permanent magnet synchronous machine (IPMSM), which has distributed armature windings. In order to study the iron loss with the effect of slot-pole combination, two machines for high-speed operation such as electric vehicle were designed. One is fractional slot winding and the other is conventional one. In the analysis, we developed a new iron loss model of electrical machines for high-speed operation. The calculated iron loss was compared with the experimental data. It was clarified that the proposed method can estimate iron loss effectively at high-speed operation. Based on this newly proposed method, we analyzed iron loss of the two machines according to their driving conditions. From the analysis results, it was shown that rotor iron loss of machine employing fractional slot-winding is definitely large at load condition. In addition, it is interesting that the ratio (rotor iron loss/stator iron loss) becomes larger as the speed of the machines increase if the number of slot per pole is fractional.

Effective or Efficient ? Effects of Beijing ’ s Vehicle Lottery System on Fleet Composition and Environment * <=SEP=> To control vehicle growth and air pollution, Beijing’s municipal government imposed a vehicle lottery system in 2011, which randomly allocated a quota of licenses to potential buyers. This paper investigates the effect of this policy on fleet composition, fuel consumption, air pollution, and social welfare. Using car registration data, we estimate a random coefficient discrete choice model and conduct counterfactual analysis based on the estimated parameters. We find that the lottery reduced new passenger vehicle *We gratefully acknowledge the kind help and constructive comments of Professors Andrew Cassey, Ana Espinola-Arredondo, Benjamin Cowan, Gregmar Galinato, Dong Lu, Jill McCluskey, Mark Gibson, Shanjun Li, Jia Yan, Dan Yang and seminar participants at Washington State University. Financial support from the China National Science Fund (grant #71620107005) is acknowledged. †Corresponding author. Email: yziying1988@gmail.com (Z. Yang), fmunoz@wsu.edu (F. Muñoz-García), tangmp.sicau@gmail.com (M. Tang). sales by 50.15%, fuel consumption by 48.69%, and pollutant emissions by 48.69% in 2012. Also, such lottery shifted new auto purchases towards high-end but less fuel efficient vehicles. In our counterfactual analysis, we show that a progressive tax scheme works better than the lottery system at decreasing fuel consumption and air pollution, and leads to a higher fleet fuel efficiency and less welfare loss.

Smoothed Sarsa: Reinforcement learning for robot delivery tasks <=SEP=> Our goal in this work is to make high level decisions for mobile robots. In particular, given a queue of prioritized object delivery tasks, we wish to find a sequence of actions in real time to accomplish these tasks efficiently. We introduce a novel reinforcement learning algorithm called Smoothed Sarsa that learns a good policy for these delivery tasks by delaying the backup reinforcement step until the uncertainty in the state estimate improves. The state space is modeled by a Dynamic Bayesian Network and updated using a Region-based Particle Filter. We take advantage of the fact that only discrete (topological) representations of entity locations are needed for decision-making, to make the tracking and decision making more efficient. Our experiments show that policy search leads to faster task completion times as well as higher total reward compared to a manually crafted policy. Smoothed Sarsa learns a policy orders of magnitude faster than previous policy search algorithms. We demonstrate our results on the Player/Stage simulator and on the Pioneer robot.

A Wideband Dual-Polarized Dielectric Magnetoelectric Dipole Antenna <=SEP=> A new wideband ±45° dual-polarized metal loop dielectric resonator magnetoelectric antenna is proposed in this communication. The antenna consists of two orthogonal dielectric bars that support two orthogonal electric dipoles and two cross interlaced metal semi-loops that are equivalent to two orthogonal magnetic dipoles above the ground. With a legitimate combination of the electric and magnetic dipoles, a unidirectional radiation with low backward radiation and equal E-plane and H-plane radiation patterns can be achieved. The antenna can be made of a monolithic dielectric block with simple installation. To validate the new antenna configuration, a prototype is designed, manufactured, and measured. The prototype antenna using ceramic with the electrical size of <inline-formula> <tex-math notation="LaTeX">$0.33\lambda _{o} \times 0.33\lambda _{o} \times 0.21\lambda _{o}$ </tex-math></inline-formula> demonstrates that an impedance bandwidth of 11.4% (<inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert < -15$ </tex-math></inline-formula> dB) in the 3.5 GHz frequency band can be achieved with the measured forward/backward ratio of better than 22 dB. The proposed antenna is suitable as antenna element for massive-MIMO array antennas, where a large number of antenna elements need to be installed in a compact space.

Neural Monkey: The Current State and Beyond <=SEP=> Neural Monkey is an open-source toolkit for sequence-to-sequence learning. The focus of this paper is to present the current state of the toolkit to the intended audience, which includes students and researchers, both active in the deep learning community and newcomers. For each of these target groups, we describe the most relevant features of the toolkit, including the simple configuration scheme, methods of model inspection that promote useful intuitions, or a modular design for easy prototyping. We summarize relevant contributions to the research community which were made using this toolkit and discuss the characteristics of our toolkit with respect to other existing systems. We conclude with a set of proposals for future development.

Introduction to the talking points project <=SEP=> A number of systems, technological and otherwise, exist for helping blind and visually impaired users navigate from place to place. [5, 3] However, in focusing only on the destination, these systems often neglect the journey. We propose to rectify this, with a device to improve a user’s peripheral awareness of their surroundings. This is the motivation and the primary goal of this project. The Talking Points project aims to create a system for attaching information to places and objects, in a format that can be easily converted to speech. It allows blind and visually impaired users to get information about the places and objects they are walking by, and can also be used to provide digital information to other passersby. We have created a proof-of-concept of this system. The system reads passive RFID tags from a mobile reader. It looks up a tag’s ID number to find associated text, then uses text-to-speech software to read that speech to the user.

CAST: Collaborative Agents for Simulating Teamwork <=SEP=> Psychological studies on teamwork have shown that an effective team often can anticipate information needs of teammates based on a shared mental model. Existing multi-agent models for teamwork are limited in their ability to support proactive information exchange among teammates. To address this issue, we have developed and implemented a multi-agent architecture called CAST that simulates teamwork and supports proactive information exchange in a dynamic environment. We present a formal model for proactive information exchange. Knowledge regarding the structure and process of a team is described in a language called MALLET. Beliefs about shared team processes and their states are represented using Petri Nets. Based on this model, CAST agents offer information proactively to those who might need it using an algorithm called DIARG. Empirical evaluations using a multi-agent synthetic testbed application indicate that CAST enhances the effectiveness of teamwork among agents without sacrificing a high cost for communications.

Community Detection in Networks using Node Attributes and Modularity <=SEP=> Community detection in network is of vital importance to find cohesive subgroups. Node attributes can improve the accuracy of community detection when combined with link information in a graph. Community detection using node attributes has not been investigated in detail. To explore the aforementioned idea, we have adopted an approach by modifying the Louvain algorithm. We have proposed Louvain-ANDAttribute (LAA) and Louvain-OR-Attribute (LOA) methods to analyze the effect of using node attributes with modularity. We compared this approach with existing community detection approaches using different datasets. We found the performance of both algorithms better than Newman’s Eigenvector method in achieving modularity and relatively good results of gain in modularity in LAA than LOA. We used density, internal and external edge density for the evaluation of quality of detected communities. LOA provided highly dense partitions in the network as compared to Louvain and Eigenvector algorithms and close values to Clauset. Moreover, LOA achieved few numbers of edges between communities. Keywords—Community Detection; Louvain algorithm; Node attributes; and Modularity