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Collective Behavior of social Networking Sites
2014
IOSR Journal of Computer Engineering
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In this work, we predict collective behavior in social media. In particular, given information about some individuals, how can we infer the behavior of unobserved individuals in the same network? ...
The scale of these networks entails scalable learning of models for collective behavior prediction. ...
Acknowledgments We are done project on collective behaviour of social networking sites our college Jspm's BSIOTR. ...
doi:10.9790/0661-16227579
fatcat:xhdg3ob2vbc6zggpo4icyqdjwm
Detecting Network Intrusion through a Deep Learning Approach
2018
International Journal of Computer Applications
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In Anomaly Detection, perturbations of normal behavior suggest the presence of intentionally or unintentionally induced attacks, faults, defects, etc. ...
Intrusion Detection: collection of techniques that are used to identify attacks on the computers and network infrastructures. Anomaly detection, which is a key element of intrusion detection. ...
The traffic data for labeled dataset can be collected in a confined, isolated and private network environment. Classification on labeled data. ...
doi:10.5120/ijca2018916270
fatcat:wgj5bykn6fahze2bopd4lkaxmm
Scalable Learning of Collective Behavior
2012
IEEE Transactions on Knowledge and Data Engineering
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This study of collective behavior is to understand how individuals behave in a social networking environment. ...
In this work, we aim to learn to predict collective behavior in social media. ...
ACKNOWLEDGMENTS This research is, in part, sponsored by the Air Force Office of Scientific Research Grant FA95500810132. The inequality (9) is derived following the Cauchy-Schwarz inequality [41] . ...
doi:10.1109/tkde.2011.38
fatcat:otx6omfsorhr3ev7cueun6j5i4
Scalable learning of collective behavior based on sparse social dimensions
2009
Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09
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The study of collective behavior is to understand how individuals behave in a social network environment. ...
In this work, we aim to learn to predict collective behavior in social media. ...
In this work, we attempt to utilize the behavior correlation presented in a social network to predict the collective behavior in social media. ...
doi:10.1145/1645953.1646094
dblp:conf/cikm/TangL09
fatcat:dpd2flllfzbzhf6sy75hncsqqy
Valkyrie: Behavioral malware detection using global kernel-level telemetry data
2015
2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP)
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In this paper, we present Valkyrie, a classification system that is able to identify malicious binaries purely based on behavioral traits gathered from large-scale telemetry submitted by endhosts using ...
In addition, since Valkyrie conducts all its heavy computation in the cloud, it therefore imposes minimal load on endpoints. ...
CONCLUSION In this paper we have presented a novel classification system for identifying malicious Portable Executable files based on behavioral data called Valkyrie. ...
doi:10.1109/mlsp.2015.7324334
dblp:conf/mlsp/KrasserMC15
fatcat:6s5dj3arfvb27jvln6agadecz4
A hidden treasure? Evaluating and extending latent methods for link-based classification
2014
Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014)
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Research on link-based classification (LBC) has studied how to leverage these connections to improve classification accuracy. This research broadly falls into two groups. ...
First, there are methods that use the original attributes and/or links of the network, via a link-aware supervised classifier or via a nonlearning method based on label propagation or random walks. ...
., words in the page) to predict its label. In contrast, link-based classification (LBC) [1] , [2] also uses, for each node, the attributes or labels of neighboring pages as model features. ...
doi:10.1109/iri.2014.7051954
dblp:conf/iri/FlemingMM14
fatcat:fsloomre35dnfajm4xmlmcuuv4
Structural Neighborhood Based Classification of Nodes in a Network
2016
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16
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Classification of entities based on the underlying network structure is an important problem. Networks encountered in practice are sparse and have many missing and noisy links. ...
For classifying a node, we take a random walk from the node and make a decision based on how nodes in the respective k th -level neighborhood are labeled. ...
the node under consideration based on the collective behavior of its first-level neighborhood. ...
doi:10.1145/2939672.2939782
dblp:conf/kdd/NandanwarM16
fatcat:7bsjpkkuoff75nhrwvuaoqfoem
Labels or attributes?
2013
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13
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We show, however, that when the network is sparsely labeled, "relational classification" based on neighbor attributes often has higher accuracy than "collective classification" based on neighbor labels ...
Link-based classification; statistical relational learning; semisupervised learning; collective inference; social networks LINK-BASED CLASSIFICATION Assume we are given a graph G = (V, E, X, Y, C) where ...
., xjM |yi)
Labels or Attributes? Rethinking the Neighbors for Collective Classification in Sparsely-Labeled Networks 1. REPORT DATE
NOV 2013
2. REPORT TYPE
3. ...
doi:10.1145/2505515.2505628
dblp:conf/cikm/McDowellA13
fatcat:gh63u64cczbyxfdpibfyz5tuhq
Behavior Based Social Dimensions Extraction for Multi-Label Classification
2016
PLoS ONE
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In this paper, we propose a novel behavior based social dimensions extraction method to improve the classification performance in multi-label heterogeneous networks. ...
Classification based on social dimensions is commonly used to handle the multi-label classification task in heterogeneous networks. ...
In this paper, we propose a novel social dimension extraction method, BBSD (Behavior based SocioDim), to handle multi-label classification task in the network. ...
doi:10.1371/journal.pone.0152857
pmid:27049849
pmcid:PMC4822808
fatcat:pqwy6mbgrzf6bl64twphbusyua
Region-based convolutional neural network using group sparse regularization for image sentiment classification
2019
EURASIP Journal on Image and Video Processing
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Motivated by these observations, we design a region-based convolutional neural network using group sparse regularization for image sentiment classification: R-CNNGSR. ...
Experiment results demonstrate that our proposed R-CNNGSR significantly outperforms the state-ofthe-art methods in image sentiment classification. ...
Authors' contributions All authors took part in the discussion of the work described in this paper. The author HX wrote the first version of the paper and did part of the experiments of the paper. ...
doi:10.1186/s13640-019-0433-8
fatcat:6fpjrmssqvakxm3uw34jyroaye
Learning with multi-resolution overlapping communities
2012
Knowledge and Information Systems
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A recent surge of participatory web and social media has created a new laboratory for studying human relations and collective behavior on an unprecedented scale. ...
propose to zoom into a network at multiple different resolutions and determine which communities reflect a targeted behavior. ...
This work is, in part, sponsored by AFOSR and ONR. ...
doi:10.1007/s10115-012-0555-0
fatcat:kufnddbugrbzfkfgta2ngiqzcq
Music Classification By Transductive Learning Using Bipartite Heterogeneous Networks
2014
Zenodo
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Bipartite networks have appeared as an alternative to similarity-based networks in sparse domains such as texts [9, 10] . ...
Bipartite heterogeneous networks have appeared as an alternative to similarity-based networks in sparse domains, such as text mining [9, 10] . ...
doi:10.5281/zenodo.1418264
fatcat:ic7iwvnaeva6nke6ekvhq4djfy
Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey
[article]
2019
arXiv
pre-print
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Firstly, we revisit the surveys done in the last 10 years in this field. ...
In this paper, we present a survey on relevant visual surveillance related researches for anomaly detection in public places, focusing primarily on roads. ...
Bayesian network estimates the posterior probability of observing a class label from a set of normal class labels and the anomaly class labels, given a test data instance. ...
arXiv:1901.08292v1
fatcat:qehtkb2imfbmpfahkgsjrx7544
A multi-resolution approach to learning with overlapping communities
2010
Proceedings of the First Workshop on Social Media Analytics - SOMA '10
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human relations and collective behavior on an unprecedented scale. ...
propose to zoom into a network at multiple dierent resolutions and determine which communities are informative of a targeted behavior. ...
or behaviors of others in the network? ...
doi:10.1145/1964858.1964861
dblp:conf/kdd/TangWLW10
fatcat:zjyna7aj4fdyzefbiobt52bouy
Network Model Selection for Task-Focused Attributed Network Inference
[article]
2017
arXiv
pre-print
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Often these relationships are explicitly given, or we must learn a representation which generalizes and predicts observed behavior in underlying individual data (e.g. attributes or labels). ...
in our experiments. ...
Tasks for Evaluating Network Models We evaluate network models on two fundamental network tasks: collective classification and link prediction.
1) Collective classification (CC): The collective classification ...
arXiv:1708.06303v2
fatcat:vbadmkkyhbfn5h6eiooaimhvxi
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