Community finding within the community set space

Author(s):  
Jerry Scripps ◽  
Christian Trefftz
Keyword(s):  
Author(s):  
Jennifer Morton

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.


Author(s):  
Stefano Tardini

The notion of community is pivotal in the sociological tradition. According to Nisbet (1966), “the most fundamental and far-reaching of sociology’s unit ideas is community” (p. 47). Yet, it is not easy to define what a community is. Though in everyday life the concept of “community” is widespread, nonetheless this concept is very problematic in scientific reflections, partly because of its strongly interdisciplinary nature. As long ago as 1955, Hillery could list and compare 94 different definitions of “community,” finding only some common elements among them, such as social interaction, area, and common ties. Generally speaking, a community can be defined as “a group of persons who share something more or less decisive for their life, and who are tied by more or less strong relationships” (Cantoni & Tardini, 2006, p. 157). It is worth noticing here that the term “community” seems to have only favorable connotations. As observed in 1887 by Ferdinand Tönnies, the German sociologist who first brought the term “community” into the scientific vocabulary of the social sciences, “a young man is warned about mixing with bad society: but ‘bad community’ makes no sense in our language” (Tönnies, 2001, p. 18; Williams, 1983). Two main ways of considering communities can be singled out: 1. Communities can be intended as a set of people who have something in common, and 2. Communities can be intended as groups of people who interact. The distinction between the two ways of conceiving a community is very well illustrated by an example provided by Aristotle. In his Politics (3.1.12), the Greek philosopher tells that, when Babylon was captured by an invading army of Persians, in certain parts of the city the capture itself had not been noticed for three days. This is the reason why Aristotle considers Babylon not a polis, but an ethnos. In fact, according to Aristotle, what distinguishes the polis, that is, the perfect form of community (see Politics 1.1.1), from the ethnos is the presence of interactions and communications among the citizens. In a polis citizens speak to each other, they interact and communicate, while in an ethnos they just have the same walls in common. In the sense of the ethnos, we speak, for instance, of the community of the linguists, of the community of Italian speaking people, of the open source community, and so on. The members of such communities usually do not know each other, they do not communicate each with all the others, but they have the perception of belonging to the community, they are aware of being part of it. According to Cohen (1985), such communities are symbolic constructions. Rather than being structures, they are entities of meaning, founded on a shared conglomeration of normative codes and values that provide community members with a sense of identity. In a similar way, Anderson (1991) defines the modern nations (the Aristotelian ethne) as “imagined communities”: [They are] imagined because the members of even the smallest nation will never know most of their fellowmembers, meet them, or even hear of them, yet in the minds of each lives the image of their communion. […] In fact, all communities larger than primordial villages or face-to-face contact (and perhaps even these) are imagined. (pp. 5-6)


2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shu-Chuan Chu ◽  
Lili Chen ◽  
Sachin Kumar ◽  
Saru Kumari ◽  
Joel J. P. C. Rodrigues ◽  
...  

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.


2007 ◽  
Vol 18 (06) ◽  
pp. 937-947 ◽  
Author(s):  
F. A. RODRIGUES ◽  
G. TRAVIESO ◽  
L. da F. COSTA

A new method for community identification is proposed which is founded on the analysis of successive neighborhoods, reached through hierarchical growth from a starting vertex, and on the definition of communities as a subgraph whose number of inner connections is larger than outer connections. In order to determine the precision and speed of the method, it is compared with one of the most popular community identification approaches, namely Girvan and Newman's algorithm. Although the hierarchical growth method is not as precise as Girvan and Newman's method, it is potentially faster than most community finding algorithms.


2019 ◽  
Vol 16 (2) ◽  
pp. 595-600
Author(s):  
S. Venkatesh ◽  
P. Thangaraj

Chalk and talk methods of teaching have been effective till the technological expansion has rooted up for student community. Finding some way to be distracted in a classroom, teaching and learning strategies needed an efficient approach for sharing/transfer of knowledge. An approach reported to be dynamic in design and supportive for 24/7 discussions with measures to monitor the progress periodically is proposed in this paper. DoDo takes an innovative concept of brute force method to prevent the missing of important points during a session. Exhaustion by mentioning every other standard in a concept of discussion enhances the level of learning. Accepting to the fact that not a 100 percent is prepared for sessions nor delivered in a classroom provides loopholes and justification for knowledge not to be shared. Assignments, tests and evaluations are supposed to identify the progress of students' skills rather rank them accordingly and categorize them. DoDo ensures the deliverance of concepts to every student irrespective of his/understanding capability, tests them with easier methodologies, provides a ground for practices and monitor them every now and then with a periodical report to explain the headway of indulgence.


2015 ◽  
Vol 11 (6) ◽  
pp. 306160 ◽  
Author(s):  
Dongming Chen ◽  
Yanlin Dong ◽  
Xinyu Huang ◽  
Haiyan Chen ◽  
Dongqi Wang

Author(s):  
Helen Oosthuizen

How does music therapy engage diversity? My participation within three different South African communities offers possibilities, questions and thoughts to music therapists as we form our profession in this country and perhaps also globally. In a diverse, transient community, music is able to draw people together and may help to reconcile our many differences, but can also highlight the fragmentation of this community if all individuals and groups are not considered. As I introduce music therapy to an affluent school community, I find the cultural understandings I share with community members a helpful advantage, and yet I need to consider that by working only in wealthy, resourced communities similar to my own community, I may be highlighting the divide between wealth and poverty. In this way, I compound our countries' struggle with social inequality. As I initiate a short term music therapy group in a community very different to my own, I struggle with questions of whether music therapy has any relevance here, and find myself adapting my thinking, and working closely with the community to form a music therapy practice that has value in this context. These diverse work experiences challenge music therapists to increase our awareness of pertinent national and global issues and the possibilities our profession holds for addressing these issues. We need to explore new communities whilst continually reflecting and questioning all that we do and sharing our different work experiences with one another. Otherwise, whilst our work may hold much value within a particular community, we may find ourselves addressing or compounding national or global issues and may be growing or inhibiting our profession.


BMC Cancer ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Md. Zubbair Malik ◽  
Keilash Chirom ◽  
Shahnawaz Ali ◽  
Romana Ishrat ◽  
Pallavi Somvanshi ◽  
...  

Abstract 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.


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