Related Work hinzugefügt

documents/Proseminar-Netzwerkanalyse/Ausarbeitung-Thoma.tex

@@ -61,6 +61,9 @@
 \section{Einleitung}
 \input{Einleitung}
 
+\section{Related Work}
+\input{Related-Work}
+
 \section{DYCOS}
 \input{DYCOS-Algorithmus}
 

documents/Proseminar-Netzwerkanalyse/DYCOS-Algorithmus.tex

@@ -99,8 +99,7 @@ \subsection{Überblick}
 Die Vokabularbestimmung kann zu jedem Zeitpunkt $t$ durchgeführt 
 werden, muss es aber nicht.
 
-In \cref{alg:DYCOS} wird der DYCOS-Algorithmus als 
-Pseudocode vorgestellt:
+In \cref{alg:DYCOS} steht der DYCOS-Algorithmus in Form von Pseudocode:
 In \cref{alg1:l8} wird für jeden unbeschrifteten Knoten
 durch die folgenden Zeilen eine Beschriftung gewählt.
 

documents/Proseminar-Netzwerkanalyse/Related-Work.tex

@@ -0,0 +1,24 @@
+%!TEX root = Ausarbeitung-Thoma.tex
+Sowohl das Problem der Knotenklassifikation, als auch das der Textklassifikation,
+wurde bereits in verschiedenen Kontexten. Jedoch scheien bisher entweder nur die Struktur des zugrundeliegenden Graphen oder nur Eigenschaften der Texte verwendet worden zu sein.
+
+So werden in \cite{bhagat,szummer} unter anderem Verfahren zur Knotenklassifikation
+beschrieben, die wie der in \cite{aggarwal2011} vorgestellte DYCOS-Algorithmus,
+um den es in dieser Ausarbeitung geht, auch auf Random Walks basieren.
+
+Obwohl es auch zur Textklassifikation einige Paper gibt \cite{Zhu02learningfrom,Jiang2010302}, geht doch keines davon auf den Spezialfall der Textklassifikation
+mit einem zugrundeliegenden Graphen ein.
+
+Die vorgestellten Methoden zur Textklassifikation variieren außerdem sehr stark.
+Es gibt Verfahren, die auf dem bag-of-words-Modell basieren \cite{Ko:2012:STW:2348283.2348453}
+wie es auch im DYCOS-Algorithmus verwendet wird. Aber es gibt auch Verfahren,
+die auf dem Expectation-Maximization-Algorithmus basieren \cite{Nigam99textclassification}
+oder Support Vector Machines nutzen \cite{Joachims98textcategorization}.
+
+Es wäre also gut Vorstellbar, die Art und Weise wie die Texte in die Klassifikation
+des DYCOS-Algorithmus einfließen zu variieren. Allerdings ist dabei darauf hinzuweisen,
+dass die im Folgeden vorgestellte Verwendung der Texte sowohl einfach zu implementieren
+ist und nur lineare Vorverarbeitungszeit in Anzahl der Wörter des Textes hat, 
+als auch es erlaubt einzelne
+Knoten zu klassifizieren, wobei der Graph nur lokal um den zu klassifizerenden
+Knoten betrachten werden muss.

documents/Proseminar-Netzwerkanalyse/literatur.bib

@@ -45,6 +45,17 @@ @inproceedings{DBLP:conf/kdd/BhagatCR07
   crossref  = {DBLP:conf/kdd/2007web},
   bibsource = {DBLP, http://dblp.uni-trier.de}
 }
+
+@article{DBLP:journals/corr/abs-1101-3291,
+  author    = {Smriti Bhagat AND Graham Cormode AND S. Muthukrishnan},
+  title     = {Node Classification in Social Networks},
+  journal   = {CoRR},
+  volume    = {abs/1101.3291},
+  year      = {2011},
+  ee        = {http://arxiv.org/abs/1101.3291},
+  bibsource = {DBLP, http://dblp.uni-trier.de}
+}
+
 @proceedings{DBLP:conf/kdd/2007web,
   editor    = {Haizheng Zhang AND
                Myra Spiliopoulou AND
@@ -109,14 +120,14 @@ @MASTERSTHESIS{heck
 }
 
 @MASTERSTHESIS{Lavesson,
-  AUTHOR = {Lavesson, Niklas},
-  TITLE = {Evaluation and analysis of supervised learning algorithms and classifiers},
-  SCHOOL = {Blekinge Institute of Technology},
-  TYPE = {Diploma Thesis},
+  AUTHOR  = {Lavesson, Niklas},
+  TITLE   = {Evaluation and analysis of supervised learning algorithms and classifiers},
+  SCHOOL  = {Blekinge Institute of Technology},
+  TYPE    = {Diploma Thesis},
   ADDRESS = {Sweden},
-  MONTH = DEC,
-  YEAR = 2006,
-  PDF = {http://www.bth.se/fou/Forskinfo.nsf/Sok/c655a0b1f9f88d16c125714c00355e5d/$file/Lavesson_lic.pdf}
+  MONTH   = DEC,
+  YEAR    = 2006,
+  PDF     = {http://www.bth.se/fou/Forskinfo.nsf/Sok/c655a0b1f9f88d16c125714c00355e5d/$file/Lavesson_lic.pdf}
 }
 
 @article{Stone1974,
@@ -157,8 +168,6 @@ @incollection{porter
  address    = {San Francisco, CA, USA},
 } 
 
-
-
 @incollection{szummer,
 title       = {Partially labeled classification with Markov random walks},
 author      = {Martin Szummer and Jaakkola, Tommi},
@@ -168,3 +177,107 @@ @incollection{szummer
 year        = {2001},
 url         = {http://media.nips.cc/nipsbooks/nipspapers/paper_files/nips14/AA36.pdf},
 }
+
+@incollection{dynamic,
+title     ={Dynamic Label Propagation in Social Networks},
+author    ={Du, Juan AND Zhu, Feida AND Lim, Ee-Peng},
+booktitle ={Database Systems for Advanced Applications},
+editor    ={Meng, Weiyi AND Feng, Ling AND Bressan, Stéphane AND Winiwarter, Werner AND Song, Wei},
+pages     ={194-209},
+year      ={2013},
+isbn      ={978-3-642-37449-4},
+volume    ={7826},
+series    ={Lecture Notes in Computer Science},
+doi       ={10.1007/978-3-642-37450-0_14},
+url       ={http://dx.doi.org/10.1007/978-3-642-37450-0_14},
+publisher ={Springer Berlin Heidelberg},
+}
+
+@TECHREPORT{Zhu02learningfrom,
+    author      = {Xiaojin Zhu and Zoubin Ghahramani},
+    title       = {Learning from Labeled and Unlabeled Data with Label Propagation},
+    institution = {Carnegie Mellon University},
+    year        = {2002}
+}
+
+@TECHREPORT{Seeger01learningwith,
+    author = {Matthias Seeger},
+    title = {Learning with Labeled and Unlabeled Data},
+    institution = {University of Edinburgh},
+    year = {2001}
+}
+
+@article{Kazienko2012199,
+  title    = "Label-dependent node classification in the network ",
+  journal  = "Neurocomputing ",
+  volume   = "75",
+  number   = "1",
+  pages    = "199 - 209",
+  year     = "2012",
+  note     = "Brazilian Symposium on Neural Networks (SBRN 2010) International Conference on Hybrid Artificial Intelligence Systems (HAIS 2010) ",
+  issn     = "0925-2312",
+  doi      = "http://dx.doi.org/10.1016/j.neucom.2011.04.047",
+  url      = "http://www.sciencedirect.com/science/article/pii/S092523121100508X",
+  author   = "Przemyslaw Kazienko and Tomasz Kajdanowicz",
+  keywords = "Classification",
+  keywords = "Node classification",
+  keywords = "Label-dependent classification",
+  keywords = "Label-dependent features",
+  keywords = "Collective classification",
+  keywords = "Classification in networks",
+  keywords = "\{LDBootstrapping\}",
+  keywords = "\{LDGibbs\}",
+  keywords = "Bootstrapping",
+  keywords = "Gibbs sampling "
+}
+
+@MISC{Joachims98textcategorization,
+    author = {Thorsten Joachims},
+    title  = {Text Categorization with Support Vector Machines: Learning with Many Relevant Features},
+    year   = {1998}
+}
+
+@INPROCEEDINGS{Nigam99textclassification,
+    author    = {Kamal Nigam and Andrew Kachites Mccallum and Sebastian Thrun and Tom Mitchell},
+    title     = {Text Classification from Labeled and Unlabeled Documents using EM},
+    booktitle = {Machine Learning},
+    year      = {1999},
+    pages     = {103--134}
+}
+
+@article{Jiang2010302,
+title    = "Text classification using graph mining-based feature extraction ",
+journal  = "Knowledge-Based Systems ",
+volume   = "23",
+number   = "4",
+pages    = "302 - 308",
+year     = "2010",
+note     = "Artificial Intelligence 2009 AI-2009 The 29th \{SGAI\} International Conference on Artificial Intelligence ",
+issn     = "0950-7051",
+doi      = "http://dx.doi.org/10.1016/j.knosys.2009.11.010",
+url      = "http://www.sciencedirect.com/science/article/pii/S095070510900152X",
+author   = "Chuntao Jiang and Frans Coenen and Robert Sanderson and Michele Zito",
+keywords = "Text classification",
+keywords = "Graph representation",
+keywords = "Graph mining",
+keywords = "Weighted graph mining",
+keywords = "Feature extraction "
+}
+
+@inproceedings{Ko:2012:STW:2348283.2348453,
+ author    = {Ko, Youngjoong},
+ title     = {A Study of Term Weighting Schemes Using Class Information for Text Classification},
+ booktitle = {Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval},
+ series    = {SIGIR '12},
+ year      = {2012},
+ isbn      = {978-1-4503-1472-5},
+ location  = {Portland, Oregon, USA},
+ pages     = {1029--1030},
+ numpages  = {2},
+ url       = {http://doi.acm.org/10.1145/2348283.2348453},
+ doi       = {10.1145/2348283.2348453},
+ acmid     = {2348453},
+ publisher = {ACM},
+ address   = {New York, NY, USA},
+ keywords  = {idf, term weighting, text classification},
+}