The inherent community structure of hyperbolic networks

A remarkable approach for grasping the relevant statistical features of real networks with the help of random graphs is offered by hyperbolic models, centred around the idea of placing nodes in a low-dimensional hyperbolic space, and connecting node pairs with a probability depending on the hyperbolic distance. It is widely appreciated that these models can generate random graphs that are small-world, highly clustered and scale-free at the same time; thus, reproducing the most fundamental common features of real networks. In the present work, we focus on a less well-known property of the popularity-similarity optimisation model and the $${\mathbb {S}}^1/{\mathbb {H}}^2$$ S 1 / H 2 model from this model family, namely that the networks generated by these approaches also contain communities for a wide range of the parameters, which was certainly not an intention at the design of the models. We extracted the communities from the studied networks using well-established community finding methods such as Louvain, Infomap and label propagation. The observed high modularity values indicate that the community structure can become very pronounced under certain conditions. In addition, the modules found by the different algorithms show good consistency, implying that these are indeed relevant and apparent structural units. Since the appearance of communities is rather common in networks representing real systems as well, this feature of hyperbolic models makes them even more suitable for describing real networks than thought before.

Overlapping community finding with noisy pairwise constraints

In many real applications of semi-supervised learning, the guidance provided by a human oracle might be “noisy” or inaccurate. Human annotators will often be imperfect, in the sense that they can make subjective decisions, they might only have partial knowledge of the task at hand, or they may simply complete a labeling task incorrectly due to the burden of annotation. Similarly, in the context of semi-supervised community finding in complex networks, information encoded as pairwise constraints may be unreliable or conflicting due to the human element in the annotation process. This study aims to address the challenge of handling noisy pairwise constraints in overlapping semi-supervised community detection, by framing the task as an outlier detection problem. We propose a general architecture which includes a process to “clean” or filter noisy constraints. Furthermore, we introduce multiple designs for the cleaning process which use different type of outlier detection models, including autoencoders. A comprehensive evaluation is conducted for each proposed methodology, which demonstrates the potential of the proposed architecture for reducing the impact of noisy supervision in the context of overlapping community detection.

Community-Centric Graph Convolutional Network for Unsupervised Community Detection

Community detection, aiming at partitioning a network into multiple substructures, is practically importance. Graph convolutional network (GCN), a new deep-learning technique, has recently been developed for community detection. Markov Random Fields (MRF) has been combined with GCN in the MRFasGCN method to improve accuracy. However, the existing GCN community-finding methods are semi-supervised, even though community finding is essentially an unsupervised learning problem. We developed a new GCN approach for unsupervised community detection under the framework of Autoencoder. We cast MRFasGCN as an encoder and then derived node community membership in the hidden layer of the encoder. We introduced a community-centric dual decoder to reconstruct network structures and node attributes separately in an unsupervised fashion, for faithful community detection in the input space. We designed a scheme of local enhancement to accommodate nodes to have more common neighbors and similar attributes with similar community memberships. Experimental results on real networks showed that our new method outperformed the best existing methods, showing the effectiveness of the novel decoding mechanism for generating links and attributes together over the commonly used methods for reconstructing links alone.

Decentralized Private Information Sharing Protocol on Social Networks

Social networks are becoming popular, with people sharing information with their friends on social networking sites. On many of these sites, shared information can be read by all of the friends; however, not all information is suitable for mass distribution and access. Although people can form communities on some sites, this feature is not yet available on all sites. Additionally, it is inconvenient to set receivers for a message when the target community is large. One characteristic of social networks is that people who know each other tend to form densely connected clusters, and connections between clusters are relatively rare. Based on this feature, community-finding algorithms have been proposed to detect communities on social networks. However, it is difficult to apply community-finding algorithms to distributed social networks. In this paper, we propose a distributed privacy control protocol for distributed social networks. By selecting only a small portion of people from a community, our protocol can transmit information to the target community.

You’re Going to Need a Team

This chapter discusses the lessons for women of color undergraduate and graduate students that the author learned from participating in the McNair Scholars Program in 2009. These include the benefits of forming a community, finding the right mentor or mentors, and prioritizing a regular practice of self-care. The chapter provides firsthand examples of challenges as well as tips and possible solutions for such obstacles. In this essay, administrators and staff can find suggestions for what types of programming can help women of color prepare for graduate school and finish it. Undergraduates will find tips for what types of support they should be seeking out if they are interested in going to graduate school. Graduate students will find recommendations on how to succeed professionally and personally.

Methodology of predicting novel key regulators in ovarian cancer network: a network theoretical approach

Background Identification of key regulator/s in ovarian cancer (OC) network is important for potential drug target and prevention from this cancer. This study proposes a method to identify the key regulators of this network and their importance. Methods The protein-protein interaction (PPI) network of ovarian cancer (OC) is constructed from curated 6 hundred genes from standard six important ovarian cancer databases (some of the genes are experimentally verified). We proposed a method to identify key regulators (KRs) from the complex ovarian cancer network based on the tracing of backbone hubs, which participate at all levels of organization, characterized by Newmann-Grivan community finding method. Knockout experiment, constant Potts model and survival analysis are done to characterize the importance of the key regulators in regulating the network. Results The PPI network of ovarian cancer is found to obey hierarchical scale free features organized by topology of heterogeneous modules coordinated by diverse leading hubs. The network and modular structures are devised by fractal rules with the absence of centrality-lethality rule, to enhance the efficiency of signal processing in the network and constituting loosely connected modules. Within the framework of network theory, we device a method to identify few key regulators (KRs) from a huge number of leading hubs, that are deeply rooted in the network, serve as backbones of it and key regulators from grassroots level to complete network structure. Using this method we could able to identify five key regulators, namely, AKT1, KRAS, EPCAM, CD44 and MCAM, out of which AKT1 plays central role in two ways, first it serves as main regulator of ovarian cancer network and second serves as key cross-talk agent of other key regulators, but exhibits disassortive property. The regulating capability of AKT1 is found to be highest and that of MCAM is lowest. Conclusions The popularities of these key hubs change in an unpredictable way at different levels of organization and absence of these hubs cause massive amount of wiring energy/rewiring energy that propagate over all the network. The network compactness is found to increase as one goes from top level to bottom level of the network organization.

COMMUNITY FINDING AS LEARNING: IMPACTS OF A NOVEL MODEL FOR COMMUNITY ENGAGED, PROJECT BASED LEARNING IN ENGINEERING

This paper explores the impact of a specific implementation of community engaged engineering design pedagogy. By asking students about their experience in choosing, engaging with, and researching a community to develop an understanding of and clearly articulating a design problem in a Requests for Proposals, we seek to understand how student initiated community engaged learning (CEL) can contribute to learning about design. Results of the survey show that students did pick up skills and experiences that reflected course and activity learning objectives. Students engaged and relied on – sometimes to their detriment – personal contact and communication with stakeholder communities for information. They expressed an awareness of the importance of personal investment in the community, even if that investment was limited in their own projects. Not unexpectedly, students also reported a preference for and greater perceived learning in a more conventional design education experience. However, the act of community finding and engagement did impact their understanding of engineering design, particularly around often neglected aspects, and helped them to see design more holistically.

Moving Up without Losing Your Way

Upward mobility through the path of higher education has been an article of faith for generations of working-class, low-income, and immigrant college students. While we know this path usually entails financial sacrifices and hard work, very little attention has been paid to the deep personal compromises such students have to make as they enter worlds vastly different from their own. Measuring the true cost of higher education for those from disadvantaged backgrounds, this book looks at the ethical dilemmas of upward mobility—the broken ties with family and friends, the severed connections with former communities, and the loss of identity—faced by students as they strive to earn a successful place in society. The book reframes the college experience, factoring in not just educational and career opportunities but also essential relationships with family, friends, and community. Finding that student strivers tend to give up the latter for the former, negating their sense of self, the book seeks to reverse this course. It urges educators to empower students with a new narrative of upward mobility—one that honestly situates ethical costs in historical, social, and economic contexts and that allows students to make informed decisions for themselves. The book paves a hopeful road so that students might achieve social mobility while retaining their best selves.