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\cite{Coifman_2006} is a math theoretical article in wich it is shown that in the context of manifolds, graphs, data sets and general metric spaces, diffusion processes and Markov processes can be analyzed in  a multiscale fashion very much in the spirit of classical wavelet analysis. It is proposed fast and stable algorithms for constructing orthonormal bases for the multiscale approximation spaces, and it is shown that there is an efficient scaling function and wavelet transform.  \cite{Coifman_2011} studies harmonic analysis of databases.  \cite{Sandryhaila_2013} constructs a new signal processing type of graph filter for the classification problem.  \cite{Coifman_2011} harmonic analysis of databases  \cite{Sandryhaila_2013} \cite{Symeonidis_2013} studies link prediction in biological and social network.  \subsection{Social Networks}  \cite{Campbell_2014} studies the following problems: community detection, relational learning, leadership role prediction. Co-occurences of entities is used to form a graph from text. A more refined approach is to extract relationships fromtext. A schema with entity types and attributes is described. A graphical query language is used to analyze the entity graph.  \cite{Sizov_2010} studies authority ranking on social networks. The use of hashtags are indicative of emergent semantics in modern social networks as in \cite{Dellschaft_2009}. The authors formaly define social graphs and the application of tensors for authority ranking. A SocialWeb graph is defined as a graph G = ( V, L, E, linkType ) where V is the set of users in the community, L is the set of literals (e.g., hashtags), and E is the set of relations between users in V . Additionally, the function linkType : E -> L returns the annotation from L that relates two users. User X links to user Y by edge of type Z iff a) X follows Y (in the common sense of Twitter) and b) both X and Y have recently used the hashtag Z in their own postings/tweets. The authors describe the data collection and transformation processes for Twitter.  \cite{Choudhury_2010} defines diffusion and prediction on social media.  \cite{Altshuler_2012} analyzes  trend prediction. In \cite{Guille_2013} it is studied the information diffusion and prediction. The author surveys the research done, defines a new graph model: Time Based Asynchronous Independent Cascades (T-BaSIC) and provides an open source implementation SOcial Network DYnamics (SONDY). Definitions are provided for message topics, social influence, heard behaviour and informational cascade. A probability of information transmission between nodes is used in the model. Due to "closed world assumption" the model underestimates the volume.  \cite{Jain_2014} studies the harmonic analysis of the co-occurence matrix for short text messages.  \subsection{Spectral Information Retrieval}