Twitter related problems:

1. Named Entity Recognition (NER) \cite{Li_2012}, \cite{Caputo_2009}

2. Relation Extraction \cite{Wang_2011}

3. Classification \cite{Jain_2014}

4. Event Detection \cite{Dong_2014}, \cite{Nurwidyantoro_2013}, \cite{Pohl_2013}, \cite{Gao_2013}

5. Event Tracking \cite{Pohl_2013}

6. Geo search and visualization \cite{Gao_2013}

7. Recommender \cite{Arru_2013}, \cite{Costa_2010}

8. Trend Mining \cite{Desmier_2013}

9. Difusion of topics \cite{Guille_2013}, \cite{Altshuler_2012}, \cite{Choudhury_2010}

10. Prediction \cite{Guille_2013}, \cite{Symeonidis_2013}, \cite{Altshuler_2012}, \cite{Sizov_2010}, \cite{Choudhury_2010}

11. Emergence \cite{Miller_2013}, \cite{Jain_2014}

Theoretical aspects:

1. Attributed Graph Model \cite{Miller_2013}, \cite{Kim_2010}

2. Rezidual Analysis of Attributed Graphs \cite{Miller_2013}

3. Sub-graph matching \cite{Miller_2013}, \cite{Kriege_2012}

4. Diffusion wavelets transform \cite{Wang_2009}, \cite{Jain_2014}

5. Detection Theory \cite{Miller_2013}

6. Complex networks

7. Spectral analysis of graphs

8. Signal Processing

9. Prediction Annalytics

10. Tensors \cite{Miller_2013}

Problems addresed in the framework of DSP on graphs:

1. Mathematical model of Twitter as a dynamic attributed graph with streams attached to vertexes.

2. Subgraph matching.

3. Twitter diffusion of topics or hash terms.

4. Tweet classification.

5. Quering a corpus of dependencies parses of sentences viewed as graphs. \cite{Miller_2012}

6. Integrated search of documents, multimedia archives and geographic data.

7. Detection Theory on twitter graphs.

8. Recommender Systems. \cite{Arru:2013:SUR:2487788.2488088} + \cite{Li_2012} NER

9. Event Detection. \cite{Dong_2014}

10. Predicting Event

11. Event Summarization

12. Event Association

13. Early Warning System

14. Big Data Implementation.

Big Data Application Layer for Graphs:

1. Search/Query

2. 1. Graph Analytics

   2. PageRank

   3. Subgraph Detection

   4. Belief Propagation

   5. Clustering/Classification