Looking for a “Missing Link”: Formal Employment Services and Social Networks in Refugees’ Job Search1

2008 ◽  
Vol 29 (2) ◽  
pp. 135-152 ◽  
Author(s):  
Silvia Torezani ◽  
Val Colic-Peisker ◽  
Farida Fozdar
Keyword(s):  
Social Networks ◽  
Employment Services ◽  
Missing Link ◽  
Formal Employment

scholarly journals A novel similarity measure for missing link prediction in social networks

2020 ◽  
Vol 19 (2) ◽  
pp. 1071
Author(s):  
Gogulamudi Naga Chandrika ◽  
E. Srinivasa Reddy
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Similarity Measure ◽  
Network Theory ◽  
Link Prediction ◽  
Prediction Methods ◽  
Missing Link ◽  
The Social ◽  
Hidden Patterns ◽  
Over Time

<p><span>Social Networks progress over time by the addition of new nodes and links, form associations with one community to the other community. Over a few decades, the fast expansion of Social Networks has attracted many researchers to pay more attention towards complex networks, the collection of social data, understand the social behaviors of complex networks and predict future conflicts. Thus, Link prediction is imperative to do research with social networks and network theory. The objective of this research is to find the hidden patterns and uncovered missing links over complex networks. Here, we developed a new similarity measure to predict missing links over social networks. The new method is computed on common neighbors with node-to-node distance to get better accuracy of missing link prediction. </span><span>We tested the proposed measure on a variety of real-world linked datasets which are formed from various linked social networks. The proposed approach performance is compared with contemporary link prediction methods. Our measure makes very effective and intuitive in predicting disappeared links in linked social networks.</span></p>

scholarly journals Langkah Promosi Objek Wisata Daerah Untuk Meningkatkan Pengetahuan Masyarakat Pada Dinas Pariwisata, Pemuda Dan Olah Raga Kabupaten Magelang

2018 ◽  
Vol 9 (2) ◽  
Author(s):  
Prio Susilo ◽  
Atun Yulianto
Keyword(s):  
Social Media ◽  
Human Resources ◽  
Tourist Attraction ◽  
Employment Services ◽  
Nature Religion ◽  
Formal Employment ◽  
The Media ◽  
The Many ◽  
Real Picture ◽  
The Way

Abstract - Magelang Regency has many regional attractions that can be explored by the community, ranging from nature, religion and artificial. Until now, the number of tourist visits to Magelang Regency has not been evenly distributed on all existing tourist objects. This condition is caused by several factors, including the unknown yet other tourist objects besides the Borobudur temple in Magelang Regency. Promotional steps for regional attractions by Dinas Pariwisata, Pemuda Dan Olah Raga Kabupaten Magelang (DISPARPORA) implemented through several media, both print and electronic. This promotion step is intended to provide knowledge to the general public about the many potentials in the form of regional tourism objects owned by Magelang Regency. The presentation of this study uses descriptive qualitative methods supported by surveys to provide a real picture of activities Dinas Pariwisata, Pemuda Dan Olah Raga in implementing the steps to promote tourism in the region. The results of this study indicate that there is a shift in the promotion steps that have been carried out by DISPARPORA Magelang Regency in promoting its tourist attraction. The shift in the way this promotion lies in the media is used with tendencies carried out through online media rather than conventional (offline) methods for the years now. The way to promote it is very appropriate to use to see the tendency of today's society to use gadget tools for surfing in the virtual world through social media and other online facilities. However, there are still obstacles in carrying out this tourism promotion effort, namely the limited human resources that can manage all kinds of social media accounts and the DISPARPORA website in Magelang Regency. HR (Human Resources) is more preoccupied with formal employment services. This makes the promotion of regional tourist attraction owned by Magelang Regency not yet optimally conveyed to the community. The steps to solve this problem are by organizing training related to information technology that is currently developing. Keyword : Objects, Promotions, Tourism Abstrak - Kabupaten Magelang memiliki banyak objek wisata daerah yang dapat eksplorasi masyarakat, mulai dari alam, religi maupun buatan. Hingga saat ini jumlah kunjungan wisatawan ke Kabupaten Magelang belum merata pada keseluruhan obyek wisata yang ada. Kondisi ini disebabkan oleh beberapa faktor, antara lain belum dikenalnya obyek-obyek wisata lain selain candi Borobudur di Kabupaten Magelang. Langkah promosi bjek wisata daerah oleh Dinas Pariwisata, Pemuda Dan Olah Raga Kabupaten Magelang (DISPARPORA) dilaksanakan melaui beberapa media, baik cetak maupun elektronik. Langkah promosi ini ditujukan untuk memberikan pengetahuan kepada masyarakat luas tentang banyaknya potensi yang berupa objek wisata daerah yang dimiliki Kabupaten Magelang. Penyajian penelitian ini menggunakan metode diskriptif kualitatif yang didukung survey untuk memberikan gambaran nyata atas kegiatan Dinas Pariwisata, Pemuda Dan Olah Raga dalam melaksanakan langkah-langkah promosi wisata daerahnya. Hasil penelitian ini menunjukkan bahwa terjadi pergeseran langkah promosi yang telah dilakukan oleh DISPARPORA Kabupaten Magelang dalam mempromosikan daya tarik wisatanya. Pergeseran cara promosi ini terletak pada media yang digunakan dengan kecenderungan dilakukan melalui media online dibanding cara konvensional (offline) untuk tahun-tahun sekarang ini. Cara berpromosi ini sangat tepat digunakan melihat kecenderungan masyarakat jaman sekarang yang lebih banyak menggunakan sarana gadget untuk berselancar didunia maya melalui media sosial dan fasilitas online lainnya. Namun demikian masih ada kendala dalam menjalankan upaya promosi wisata ini yaitu keterbatasan sumber daya manusia yang dapat mengelola dengan baik segala macam account media sosial maupun website DISPARORA Kabupaten Magelang. SDM lebih banyak disibukkan dengan pekerjaan formal kedinasan. Hal ini menjadikan langkah promosi daya tarik wisata daerah yang dimiliki Kabupaten Magelang belum optimal tersampaikan kepada masyarakat. Adapun langkah penyelesaiakan masalah ini adalah dengan penyelenggaraan pelatihan-pelatihan terkait tehnologi informasi yang berkembang saat ini. Kata Kunci: Objek, Promosi, Wisata

scholarly journals Trust-Based Missing Link Prediction in Signed Social Networks with Privacy Preservation

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Huaizhen Kou ◽  
Fan Wang ◽  
Chao Lv ◽  
Zhaoan Dong ◽  
Wanli Huang ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Privacy Protection ◽  
Link Prediction ◽  
Social Relationship ◽  
Hamming Distance ◽  
Mobile Internet ◽  
User Privacy ◽  
Missing Link ◽  
Signed Social Networks

With the development of mobile Internet, more and more individuals and institutions tend to express their views on certain things (such as software and music) on social platforms. In some online social network services, users are allowed to label users with similar interests as “trust” to get the information they want and use “distrust” to label users with opposite interests to avoid browsing content they do not want to see. The networks containing such trust relationships and distrust relationships are named signed social networks (SSNs), and some real-world complex systems can be also modeled with signed networks. However, the sparse social relationships seriously hinder the expansion of users’ social circle in social networks. In order to solve this problem, researchers have done a lot of research on link prediction. Although these studies have been proved to be effective in the unsigned social network, the prediction of trust and distrust in SSN has not achieved good results. In addition, the existing link prediction research does not consider the needs of user privacy protection, so most of them do not add privacy protection measures. To solve these problems, we propose a trust-based missing link prediction method (TMLP). First, we use the simhash method to create a hash index for each user. Then, we calculate the Hamming distance between the two users to determine whether they can establish a new social relationship. Finally, we use the fuzzy computing model to determine the type of their new social relationship (e.g., trust or distrust). In the paper, we gradually explain our method through a case study and prove our method’s feasibility.

scholarly journals Missing Link Prediction Using Non-Overlapped Features and Multiple Sources of Social Networks

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 214
Author(s):  
Pokpong Songmuang ◽  
Chainarong Sirisup ◽  
Aroonwan Suebsriwichai
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Link Prediction ◽  
Classification Model ◽  
Density Level ◽  
Missing Link ◽  
Common Features ◽  
The Common

The current methods for missing link prediction in social networks focus on using data from overlapping users from two social network sources to recommend links between unconnected users. To improve prediction of the missing link, this paper presents the use of information from non-overlapping users as additional features in training a prediction model using a machine-learning approach. The proposed features are designed to use together with the common features as extra features to help in tuning up for a better classification model. The social network data sources used in this paper are Twitter and Facebook where Twitter is a main data for prediction and Facebook is a supporting data. For evaluations, a comparison using different machine-learning techniques, feature settings, and different network-density level of data source is studied. The experimental results can be concluded that the prediction model using a combination of the proposed features and the common features with Random Forest technique gained the best efficiency using percentage amount of recovering missing links and F1 score. The model of combined features yields higher percentage of recovering link by an average of 23.25% and the F1-measure by an average of 19.80% than the baseline of multi-social network source.

Multilayer Social Networks

2016 ◽  
Author(s):  
Mark E. Dickison ◽  
Matteo Magnani ◽  
Luca Rossi
Keyword(s):  
Social Networks ◽  
Multilayer Social Networks
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