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Social Network Analysis and Mining

Erschienen in:

01.12.2021 | Review Paper

Social network analysis using deep learning: applications and schemes

verfasst von: Ash Mohammad Abbas

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2021

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Abstract

Online social networks (OSNs) are part of daily life of human beings. Millions of users are connected through online social networks. Due to very large number of users and huge amount of data, social network analysis is a challenging task. The emergence of deep learning techniques has enabled to carry out a rigorous analysis of OSNs. A lot of research is carried out in the area of social network analysis using deep learning techniques from different perspectives. In this paper, we provide an overview of state-of-the-art research for different applications of social network analysis using deep learning techniques. We consider applications such as opinion analysis, sentiment analysis, text classification, recommender systems, structural analysis, anomaly detection, and fake news detection. We compare different schemes on the basis of their focus and features. Further, we point out directions for future work.

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Abd El-Jawad MH, Hodhod R, Omar YMK (2018) Sentiment analysis of social media networks using machine learning. In: 14th International Computer Engineering Conference (ICENCO), pp 174–176

Acemoglu D, Ozdaglar A (2011) Opinion dynamics and learning in social networks. Dyn Games Appl 1:3–49MathSciNetMATHCrossRef

Aktunc R, Toroslu IH, Karagoz P (2020) Event detection on communities: tracking the change in community structure within temporal communication networks. Lecture notes in social networks. Springer, Berlin

Alharthi R, Alhothali A, Moria K (2021) A real-time deep-learning approach for filtering Arabic low-quality content and accounts on Twitter. Inf Syst 99:101740CrossRef

Al-Molhem NR, Rahal Y, Dakkak M (2019) Social network analysis in telecom data. J Big Data 6:99CrossRef

Altay EV, Alatas B (2018) Detection of cyberbullying in social networks using machine learning methods. In: International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), pp 87–91

Alwehaibi A, Roy K (2018) Comparison of pre-trained word vectors for Arabic text classification using deep learning approach. In: 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp 1471–1474

Amelkin V, Bogdanov P, Singh AK (2019) A distance measure for the analysis of polar opinion dynamics in social networks. ACM Trans Knowl Discov Data 13(4):1–34CrossRef

Amine BM, Drif A, Giordano S (2019) Merging deep learning model for fake news detection. In: International Conference on Advanced Electrical Engineering (ICAEE), pp 1–4

Arasu A, Novak J, Tomlin J, Tomlin J (2002) Pagerank computation and the structure of the web: experiments and algorithms

Arya D, Worring M (2018) Exploiting relational information in social networks using geometric deep learning on hypergraphs. In: Proceedings of the ACM on International Conference on Multimedia Retrieval (ICMR). Association for Computing Machinery, New York, pp 117–125

Bai N, Meng F, Rui X, Wang Z (2021) Rumour detection based on graph convolutional neural net. IEEE Access 9:21686–21693CrossRef

Becker R, Coro F, D’Angelo G, Gilbert H (2020) Balancing spreads of influence in a social network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, no 1, pp 3–10

Beskow DM, Carley KM (2020) You are known by your friends: leveraging network metrics for bot detection in Twitter. Springer, Berlin

Bhattacharjee U (2019) Capsule network on social media text: an application to automatic detection of clickbaits. In: 11th International Conference on Communication Systems Networks (COMSNETS), pp 473–476

Campos V, Salvador A, Giro-i Nieto X, Jou B (2015) Diving deep into sentiment: understanding fine-tuned CNNs for visual sentiment prediction. In: Proceedings of the 1st International Workshop on Affect & Sentiment in Multimedia (ASM). Association for Computing Machinery, New York, pp 57–62

Chandra Y, Jana A (2020) Sentiment analysis using machine learning and deep learning. In: 7th International Conference on Computing for Sustainable Global Development (INDIACom), pp 1–4

Cheng L, Tsai S (2019) Deep learning for automated sentiment analysis of social media. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, pp 1001–1004

Conitzer V, Panigrahi D, Zhang H (2020) Learning opinions in social networks. In: III HD, Singh A (eds) Proceedings of the 37th International Conference on Machine Learning (ICML), volume 119 of Proceedings of Machine Learning Research. PMLR, pp 2122–2132

Cuomo S, Colecchia G, Piccialli F, Maiorano F (2018) Traditional and deep learning approaches to information and influence propagation in social networks. In: 14th International Conference on Signal-Image Technology Internet-Based Systems (SITIS), pp 480–484

Dessì D, Dragoni M, Fenu G, Marras M, Recupero DR (2019) Evaluating neural word embeddings created from online course reviews for sentiment analysis. In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing (SAC). Association for Computing Machinery, New York, pp 2124–2127

De A, Valera I, Ganguly N, Bhattacharya S, Gomez-Rodriguez M (2016) Learning and forecasting opinion dynamics in social networks. In: Proceedings of the 30th International Conference on Neural Information Processing Systems (NeurIPS). Curran Associates Inc., Red Hook, pp 397–405

Dinh XT, Van Pham H (2020) A proposal of deep learning model for classifying user interests on social networks. In: Proceedings of the 4th International Conference on Machine Learning and Soft Computing (ICMLSC). Association for Computing Machinery, New York, pp 10–14

Dogan E, Kaya B (2019) Text summarization in social networks by using deep learning. In: 1st International Informatics and Software Engineering Conference (UBMYK), pp 1–5

Dubova M, Moskvichev A, Goldstone R (2020) Reinforcement communication learning in different social network structures. In: Proceedings of 1st workshop on language in reinforcement learning in conjunction with International Conference on Machine Learning (ICML)

Dutta S, Masud S, Chakrabarti S, Chakraborty T (2020) Deep exogenous and endogenous influence combination for social chatter intensity prediction. Association for Computing Machinery, New York, pp 1999–2008

Fu S, Wang G, Xia S, Liu L (2020) Deep multi-granularity graph embedding for user identity linkage across social networks. Knowl-Based Syst 193:105301CrossRef

Gao T, Bao W, Li J, Gao X, Kong B, Tang Y, Chen G, Li X (2018) Dancinglines: an analytical scheme to depict cross-platform event popularity. In: Hartmann S, Ma H, Hameurlain A, Pernul G, Wagner RR (eds) Proceedings of 29th international conference on Database and Expert Systems Applications (DEXA), volume 11029 of lecture notes in computer science. Springer, pp 283–299

Garimella K, Gionis A, Parotsidis N, Tatti N (2017). Balancing information exposure in social networks. In: Proceedings of the 31st international conference on Neural Information Processing Systems (NeurIPS), Curran Associates Inc., Red Hook, pp 4666–4674

Geng X, Zhang H, Song Z, Yang Y, Luan H, Chua T-S (2014). One of a kind: user profiling by social curation. In: Proceedings of the 22nd ACM international conference on Multimedia (MM). Association for Computing Machinery, New York, pp 567–576

Gharibshah Z, Zhu X, Hainline A, Conway M (2020) Deep learning for user interest and response prediction in online display advertising. Data Sci Eng 5:12–26CrossRef

Goularas D, Kamis S (2019) Evaluation of deep learning techniques in sentiment analysis from Twitter data. In: IEEE international conference on Deep Learning and Machine Learning in emerging applications (Deep-ML). IEEE, pp 12–17

Graoui E, Zrira N, Mekouar S, Benelallam I, Bouyakhf E (2016) Outlier and anomalous behavior detection in social networks using constraint programming. In: 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA). IEEE Computer Society, Los Alamitos, pp 1–8

Guimaraes RG, Rosa RL, De Gaetano D, Rodríguez DZ, Bressan G (2017) Age groups classification in social network using deep learning. IEEE Access 5:10805–10816CrossRef

Hallac IR, Ay B, Aydin G (2018) Experiments on fine tuning deep learning models with news data for tweet classification. In: International Conference on Artificial Intelligence and Data Processing (IDAP), pp 1–5

He Q, Yang J, Shi B (2020) Constructing knowledge graph for social networks in a deep and holistic way. In: Companion Proceedings of the Web Conference (WWW). Association for Computing Machinery, New York, pp 307–308

Huang R, Ma L, He J, Chu X (2021) T-gan: a deep learning framework for prediction of temporal complex networks with adaptive graph convolution and attention mechanism. Displays 68:102023CrossRef

Hu P, He T, Chan KCC, Leung H (2017) Deep fusion of multiple networks for learning latent social communities. In: IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), pp 765–771

Ilias L, Roussaki I (2021) Detecting malicious activity in Twitter using deep learning techniques. Appl Soft Comput 107:107360CrossRef

Islam J, Zhang Y (2016) Visual sentiment analysis for social images using transfer learning approach. In: IEEE international conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom), pp 124–130

Islam MR, Muthiah S, Ramakrishnan N (2019) Rumorsleuth: joint detection of rumor veracity and user stance. In: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Association for Computing Machinery, New York, pp 131–136

Islam MR, Liu S, Wang X, Xu G (2020) Deep learning for misinformation detection on online social networks: a survey and new perspectives. Soc Netw Anal Min 10:1–20CrossRef

Jaradat S, Dokoohaki N, Matskin M, Ferrari E (2018) Learning what to share in online social networks using deep reinforcement learning. Lecture notes in social networks. Springer, Berlin

Jiang Y, Ma H, Liu Y, Li Z, Chang L (2021) Enhancing social recommendation via two-level graph attentional networks. Neurocomputing 449:71–84CrossRef

Jing N, Wu Z, Wang H (2021) A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction. Expert Syst Appl 178:115019CrossRef

Jin D, Ge M, Li Z, Lu W, He D, Fogelman-Soulie F (2017) Using deep learning for community discovery in social networks. In: IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), pp 160–167

Kapil P, Ekbal A (2020) A deep neural network based multi-task learning approach to hate speech detection. Knowl-Based Syst 210:106458CrossRef

Kazanova M (2017) Sentiment140 dataset with 1.6 million tweets: sentiment analysis with tweets

Keikha MM, Rahgozar M, Asadpour M, Abdollahi MF (2020) Influence maximization across heterogeneous interconnected networks based on deep learning. Expert Syst Appl 140:112905CrossRef

Khaled A, Ouchani S, Chohra C (2019) Recommendations-based on semantic analysis of social networks in learning environments. Comput Hum Behav 101:435–449CrossRef

Khan BA, Abbas AM (2014) Goldencrops: a software tool for analysis of a social network. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, pp 1958–1962

Khan M, Malviya A (2020) Big data approach for sentiment analysis of Twitter data using Hadoop framework and deep learning. In: International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), pp 1–5

Kumar A, Srinivasan K, Cheng W-H, Zomaya AY (2020) Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data. Inf Process Manag 57(1):102141CrossRef

Leung CK, Cuzzocrea A, Mai JJ, Deng D, Jiang F (2019) Personalized deepinf: enhanced social influence prediction with deep learning and transfer learning. In: IEEE International Conference on Big Data (Big Data), pp 2871–2880

Li J, Gao Y, Gao X, Shi Y, Chen G (2019) Senti2pop: sentiment-aware topic popularity prediction on social media. In: IEEE International Conference on Data Mining (ICDM), pp 1174–1179

Li D, Rzepka R, Ptaszynski M, Araki K (2020) Hemos: a novel deep learning-based fine-grained humor detecting method for sentiment analysis of social media. Inf Process Manag 57(6):102290CrossRef

Li B, Pi D, Lin Y (2021a) Learning ladder neural networks for semi-supervised node classification in social network. Expert Syst Appl 165:113957CrossRef

Li G, Dong M, Ming L, Luo C, Yu H, Hu X, Zheng B (2021b) Deep reinforcement learning based ensemble model for rumor tracking. Inf Syst 103:101772CrossRef

Li S, Jiang L, Wu X, Han W, Zhao D, Wang Z (2021c) A weighted network community detection algorithm based on deep learning. Appl Math Comput 401:126012MathSciNetMATH

Li X, Cao Y, Li Q, Shang Y, Li Y, Liu Y, Xu G (2021d) Rlink: deep reinforcement learning for user identity linkage. World Wide Web 24:85–103CrossRef

Li Z, Wang X, Li J, Zhang Q (2021e) Deep attributed network representation learning of complex coupling and interaction. Knowl-Based Syst 212:106618CrossRef

Liang B, Yin R, Gui L, Du J, He Y, Xu R (2020) Aspect-invariant sentiment features learning: adversarial multi-task learning for aspect-based sentiment analysis. Association for Computing Machinery, New York, pp 825–834

Liao W, Huang Y, Yang T, Wu Y (2019) Analyzing social network data using deep neural networks: a case study using twitter posts. In: IEEE International Symposium on Multimedia (ISM), pp 237–2371

Lim J, Liu Z, Zhou L (2019) Detection of fraudulent tweets: an empirical investigation using network analysis and deep learning technique. In: IEEE international conference on Intelligence and Security Informatics (ISI), pp 203–205

Lin G, Kang X, Liao K, Zhao F, Chen Y (2021) Deep graph learning for semi-supervised classification. Pattern Recognit 118:108039CrossRef

Liu L, Lu Y, Luo Y, Zhang R, Itti L, Lu J (2016) Detecting “smart” spammers on social network: a topic model approach. arXiv:1604.08504

Lu Y (2019) Social network fake account dataset: detecting smart spammers

Lucci A (2018) Huawei social network data: multinet social network

Luceri L, Braun T, Giordano S (2019) Analyzing and inferring human real-life behavior through online social networks with social influence deep learning. Appl Netw Sci 4:34CrossRef

Martinelli F, Mercaldo F, Santone A (2019) Social network polluting contents detection through deep learning techniques. In: International Joint Conference on Neural Networks (IJCNN), pp 1–10

Min S, Gao Z, Peng J, Wang L, Qin K, Fang B (2021) Stgsn—a spatial-temporal graph neural network framework for time-evolving social networks. Knowl-Based Syst 214:106746CrossRef

Molokwu BC, Kobti Z (2019) Spatial event prediction via multivariate time series analysis of neighboring social units using deep neural networks. In: International Joint Conference on Neural Networks (IJCNN), pp 1–8

Nerabie AM, AlKhatib M, Mathew SS, Barachi ME, Oroumchian F (2021) The impact of Arabic part of speech tagging on sentiment analysis: a new corpus and deep learning approach. Procedia Comput Sci 184:148–155 (The 12th International Conference on Ambient Systems, Networks and Technologies (ANT)/The 4th International Conference on Emerging Data and Industry 4.0 (EDI40)/Affiliated Workshops)CrossRef

Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). Association for Computing Machinery, New York, pp 701–710

Pham P, Nguyen LT, Vo B, Yun U (2021) Bot2vec: a general approach of intra-community oriented representation learning for bot detection in different types of social networks. Inf Syst 103:101771CrossRef

Phan N, Dou D, Wang H, Kil D, Piniewski B (2015) Ontology-based deep learning for human behavior prediction in health social networks. In: Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics (BCB). Association for Computing Machinery, New York, pp 433–442

Pota M, Esposito M, Palomino MA, Masala GL (2018) A subword-based deep learning approach for sentiment analysis of political tweets. In: 32nd international conference on Advanced Information Networking and Applications Workshops (WAINA), pp 651–656

Preethi G, Krishna PV, Obaidat MS, Saritha V, Yenduri S (2017) Application of deep learning to sentiment analysis for recommender system on cloud. In: international conference on Computer, Information and Telecommunication Systems (CITS), pp 93–97

Qawasmeh E, Tawalbeh M, Abdullah M (2019) Automatic identification of fake news using deep learning. In: Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp 383–388

Qiu J, Tang J, Ma H, Dong Y, Wang K, Tang J (2018) Deepinf: social influence prediction with deep learning. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD). Association for Computing Machinery, New York, pp 2110–2119

Rafailidis D, Crestani F (2018) Friend recommendation in location-based social networks via deep pairwise learning. In: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE Press, pp 421–428

Ramadhani AM, Goo HS (2017) Twitter sentiment analysis using deep learning methods. In: 7th International Annual Engineering Seminar (InAES). IEEE, pp 1–4

Ren Z, Shen Q, Diao X, Xu H (2021) A sentiment-aware deep learning approach for personality detection from text. Inf Process Manag 58(3):102532CrossRef

Rossi RA, Ahmed NK (2015) The network data repository with interactive graph analytics and visualization. In: AAAI

Sadr H, Pedram MM, Teshnehlab M (2020) Multi-view deep network: a deep model based on learning features from heterogeneous neural networks for sentiment analysis. IEEE Access 8:86984–86997CrossRef

Savage D, Zhang X, Yu X, Chou P, Wang Q (2014) Anomaly detection in online social networks. Soc Netw 39:62–70CrossRef

Shang L, Zhang Y, Zhang D, Wang D (2020) Fauxward: a graph neural network approach to fauxtography detection using social media comments. Soc Netw Anal Min 10:1–16CrossRef

Sinnema C, Daly AJ, Liou Y-H, Rodway J (2020) Exploring the communities of learning policy in New Zealand using social network analysis: a case study of leadership, expertise, and networks. Int J Educ Res 99:101492CrossRef

Sun C, Lv L, Tian G, Liu T (2021) Deep interactive memory network for aspect-level sentiment analysis. ACM Trans Asian Low Resour Lang Inf Process 20(1):1–12

Tan Q, Liu N, Hu X (2019) Deep representation learning for social network analysis. Front Big Data 2:2CrossRef

Tang W, Hui B, Tian L, Luo G, He Z, Cai Z (2021) Learning disentangled user representation with multi-view information fusion on social networks. Inf Fusion 74:77–86CrossRef

Thovex C (2018) Deep probabilistic learning in hidden social networks and facsimile detection. In: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE Press, pp 731–735

Tian S, Mo S, Wang L, Peng Z (2020) Deep reinforcement learning-based approach to tackle topic-aware influence maximization. Data Sci Eng 5:1–11CrossRef

Tomasi LD (2019) Youtube social network: dataset for networks, graphs analysis

Tong A, Du D-Z, Wu W (2018) On misinformation containment in online social networks. In: Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R (eds) Proceedings of international conference on Neural Information Processing Systems (NeurIPS), vol 31. Curran Associates, Inc

Tu S, Aslay C, Gionis A (2020) Co-exposure maximization in online social networks. In: Proceedings of international conference on Neural Information Processing Systems (NeurIPS)

Tzogka C, Passalis N, Iosifidis A, Gabbouj M, Tefas A (2019) Less is more: deep learning using subjective annotations for sentiment analysis from social media. In: IEEE 29th international workshop on Machine Learning for Signal Processing (MLSP), pp 1–6

Uddin AH, Bapery D, Arif ASM (2019) Depression analysis from social media data in Bangla language using long short term memory (LSTM) recurrent neural network technique. In: International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), pp 1–4

Veyseh APB, Thai MT, Nguyen TH, Dou D (2019) Rumor detection in social networks via deep contextual modeling. In: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Association for Computing Machinery, New York, pp 113–120

Vu T, Parker DS (2015) Node embeddings in social network analysis. In: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Association for Computing Machinery, New York, pp 326–329

Wan F (2019) Sentiment analysis of weibo comments based on deep neural network. In: International Conference on Communications, Information System and Computer Engineering (CISCE), pp 626–630

Wanda P, Jie HJ (2021) Deepfriend: finding abnormal nodes in online social networks using dynamic deep learning. Soc Netw Anal Min 11:1–12CrossRef

Wang J, He X, Gong Q, Chen Y, Wang T, Wang X (2018) Deep learning-based malicious account detection in the momo social network. In: 27th International Conference on Computer Communication and Networks (ICCCN), pp 1–2

Wang D, Al-Rubaie A, Hirsch B, Pole GC (2021) National happiness index monitoring using twitter for bilanguages. Soc Netw Anal Min 11:24CrossRef

Weber CT, Syed S (2019) Interdisciplinary optimism? Sentiment analysis of Twitter data. R Soc Open Sci 6(7):190473CrossRef

Wijenayake P, Silva Dd, Alahakoon D, Kirigeeganage S (2020) Automated detection of social roles in online communities using deep learning. In: Proceedings of the 3rd International Conference on Software Engineering and Information Management (ICSIM). Association for Computing Machinery, New York, pp 63–68

Wu K, Watters P, Magdon-Ismail M (2016) Network classification using adjacency matrix embeddings and deep learning. In: Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE Press, pp 299–306

Wu L, Rao Y, Yu H, Wang Y, Ambreen N (2019) A multi-semantics classification method based on deep learning for incredible messages on social media. Chin J Electron 28(4):754–763CrossRef

Wu Y, Fang Y, Shang S, Jin J, Wei L, Wang H (2021) A novel framework for detecting social bots with deep neural networks and active learning. Knowl-Based Syst 211:106525CrossRef

Yu J, Gao M, Yin H, Li J, Gao C, Wang Q (2019) Generating reliable friends via adversarial training to improve social recommendation. In: IEEE International Conference on Data Mining (ICDM), pp 768–777

Zhang A, Lipton ZC, Li M, Smola AJ (2021) Dive into deep learning

Zheng D, Wang M, Gan Q, Zhang Z, Karypis G (2020) Scalable graph neural networks with deep graph library. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD). Association for Computing Machinery, New York, pp 3521–3522

Zhou F, Liu L, Zhang K, Trajcevski G, Wu J, Zhong T (2018) Deeplink: a deep learning approach for user identity linkage. In: IEEE Conference on Computer Communications (INFOCOM), pp 1313–1321

Zou W, Hu X, Pan Z, Li C, Cai Y, Liu M (2020) Exploring the relationship between social presence and learners’ prestige in mooc discussion forums using automated content analysis and social network analysis. Comput Hum Behav 115:106582CrossRef

Titel: Social network analysis using deep learning: applications and schemes
verfasst von: Ash Mohammad Abbas
Publikationsdatum: 01.12.2021
Verlag: Springer Vienna
Erschienen in: Social Network Analysis and Mining / Ausgabe 1/2021
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-021-00799-z

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