  Graphs are irregular structures which naturally account for data integrity,
however, traditional approaches have been established outside Signal
Processing, and largely focus on analyzing the underlying graphs rather than
signals on graphs. Given the rapidly increasing availability of multisensor and
multinode measurements, likely recorded on irregular or ad-hoc grids, it would
be extremely advantageous to analyze such structured data as graph signals and
thus benefit from the ability of graphs to incorporate spatial awareness of the
sensing locations, sensor importance, and local versus global sensor
association. The aim of this lecture note is therefore to establish a common
language between graph signals, defined on irregular signal domains, and some
of the most fundamental paradigms in DSP, such as spectral analysis of
multichannel signals, system transfer function, digital filter design,
parameter estimation, and optimal filter design. This is achieved through a
physically meaningful and intuitive real-world example of geographically
distributed multisensor temperature estimation. A similar spatial multisensor
arrangement is already widely used in Signal Processing curricula to introduce
minimum variance estimators and Kalman filters \cite{HM}, and by adopting this
framework we facilitate a seamless integration of graph theory into the
curriculum of existing DSP courses. By bridging the gap between standard
approaches and graph signal processing, we also show that standard methods can
be thought of as special cases of their graph counterparts, evaluated on linear
graphs. It is hoped that our approach would not only help to demystify graph
theoretic approaches in education and research but it would also empower
practitioners to explore a whole host of otherwise prohibitive modern
applications.
