Made by Xinyu Chen • 🌐 https://xinychen.github.io
awesome-latex-drawing is a collection of 20+ academic drawing examples for using LaTeX, including Bayesian networks, function plotting, graphical models, tensor structure, and technical frameworks. [Blog post on Medium]
LaTeX is a high-quality typesetting system, and it is available as a free software. In recent years, it is very popular for creating and drawing academic graphics via LaTeX. This is because that LaTeX is able to draw many complicated graphics containing special symbols and math equations. It is really good for using LaTeX to draw certain graphics in your research.This project introduces some graphics that created by using LaTeX. It would be easy to follow the examples with a popular online LaTeX system - Overleaf. If you are interested, welcome to use Overleaf and reproduce our examples. The Overleaf is available at overleaf.com, please feel free to try it.
- Bayesian networks
- Bayesian network of Bayesian CP factorization (BCPF)
- Bayesian network of Bayesian Gaussian CP (BGCP) factorization
- Bayesian network of Bayesian augmented tensor factorization (BATF)
- Bayesian network of Bayesian temporal matrix factorization (BTMF)
- Graphical models
- Partially observed time series matrix and tensor
- Matrix and tensor time series prediction in the presence of missing values
- Undirected and circulant graphs on the relational data samples with certain degrees
- Laplacian convolutional time series modeling
pgfplots
function plotting- PDF and CDF functions of Erlang distribution
- PDF of normal distribution with different means and variances
- (Joint) PDF functions of Lognormal distributions
- Iterative process of conjugate gradient for solving a system of linear equations
- Random and non-random missing patterns
- Sine and Cosine functions
- Mechanism of time series forecasting
tikz
for matrix structure- Graphical illustration of the multivariate time series forecasting problem
- Graphical illustration of the temporal matrix factorization
- Graphical illustration of the rolling time series forecasting with temporal matrix factorization
tikz-3dplot
for tensor structure- Graphical illustration for the (origin, destination, time slot) tensor
- Graphical illustration of the classical CP tensor factorization on a third-order tensor
- Graphical illustration of augmented tensor factorization (AuTF) model
- Graphical illustration of tensor completion task and its framework
- Graphical illustration of low-rank autoregressive tensor completion model
- Graphical illustration of singular value thresholding process of the tensor data with unitary transform
- Graphical illustration of low-rank tensor completion model
- Graphical illustration of low-rank tensor regression model
Open overleaf.com in your Chrome.
It is not necessary to open each file in this repository because you can just follow this readme document.
Looking for some good LaTeX drawing examples? Here are 30+ graphics for showing how to draw in LaTaX.
LaTeX provides some powerful domain-specific packages and tools like tikz
to enable flexible graphical models. Bayesian networks represent a family of graphical models consisting of variables (usually denoted by nodes) and dependency relationships (usually denoted by arrows). Fortunately, tikz
has a specific library for drawing Bayesian networks and directed factor graphs.
Another toolbox in Python: https://docs.daft-pgm.org/en/latest/
This example is from the following paper:
- Qibin Zhao, Liqing Zhang, Andrzej Cichocki (2015). Bayesian CP factorization of incomplete tensors with automatic rank determination. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9): 1751-1763.
![drawing](https://raw.githubusercontent.com/xinychen/awesome-latex-drawing/master/BayesNet/BCPF.png)
preamble
codes:- define the
documentclass
asstandalone
, e.g.,\documentclass[border = 0.1cm]{standalone}
with 0.1cm border, - use the package
tikz
, i.e.,\usepackage{tikz}
, and usetikz
library like\usetikzlibrary{bayesnet}
which is an important tool for drawing Bayesian networks and directed factor graphs, - set the
tikz
style by using the\tikzstyle{}
command, - use math equation environments including
\usepackage{amsfonts, amsmath, amssymb}
.
- define the
body
codes:- use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - use
\node
to define nodes and text boxes in the Bayesian network, - use
\path
to define arrows in the Bayesian network, - use
\plate
to define plates in the Bayesian network.
- use
Please click on the image and check out the source code.
- Open BGCP.tex in your overleaf project, then you will see the following pictures about BGCP (Bayesian Gaussian CP decomposition) model as a Bayesian network and a directed factor graph:
This example is from the following paper:
Xinyu Chen, Zhaocheng He, Lijun Sun (2019). A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation. Transportation Research Part C: Emerging Technologies, 98: 73-84.
![drawing](https://raw.githubusercontent.com/xinychen/awesome-latex-drawing/master/BayesNet/BGCP-1.png)
preamble
codes:- define the
documentclass
asstandalone
, e.g.,\documentclass[border = 0.1cm]{standalone}
with 0.1cm border, - use the package
tikz
, i.e.,\usepackage{tikz}
, and usetikz
library like\usetikzlibrary{bayesnet}
which is an important tool for drawing Bayesian networks and directed factor graph, - set the
tikz
style by using the\tikzstyle{}
command, - use math equation environments including
\usepackage{amsmath, amsfonts, amssymb}
.
- define the
body
codes:- use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - use
\node
to define nodes and text boxes in the Bayesian network, - use
\path
to define arrows in the Bayesian network, - use
\plate
to define plates in the Bayesian network.
- use
Please click on the image and check out the source code. If you are interested in the original Bayesian network of BGCP in the paper, please check out BGCP.tex.
This example is from the following paper:
Xinyu Chen, Zhaocheng He, Yixian Chen, Yuhuan Lu, Jiawei Wang (2019). Missing traffic data imputation and pattern discovery with a Bayesian augmented tensor factorization model. Transportation Research Part C: Emerging Technologies, 104: 66-77.
![drawing](https://raw.githubusercontent.com/xinychen/awesome-latex-drawing/master/BayesNet/BATF.png)
preamble
codes:- define the
documentclass
asstandalone
, e.g.,\documentclass[border = 0.1cm]{standalone}
with 0.1cm border, - use the package
tikz
, i.e.,\usepackage{tikz}
, and usetikz
library like\usetikzlibrary{bayesnet}
which is an important tool for drawing Bayesian networks and directed factor graph, - set the
tikz
style by using the\tikzstyle{}
command, - use math equation environments including
\usepackage{amsmath, amsfonts, amssymb}
.
- define the
body
codes:- use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - use
\node
to define nodes and text boxes in the Bayesian network, - use
\path
to define arrows in the Bayesian network, - use
\plate
to define plates in the Bayesian network.
- use
Please click on the image and check out the source code.
This example is from the following paper:
Xinyu Chen, Lijun Sun (2021). Bayesian temporal factorization for multidimensional time series prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44 (9): 4659-4673.
![drawing](https://raw.githubusercontent.com/xinychen/awesome-latex-drawing/master/BayesNet/BTMF.png)
preamble
codes:- define the
documentclass
asstandalone
, e.g.,\documentclass[border = 0.1cm]{standalone}
with 0.1cm border, - use the package
tikz
, i.e.,\usepackage{tikz}
, and usetikz
library like\usetikzlibrary{bayesnet}
which is an important tool for drawing Bayesian networks and directed factor graph, - set the
tikz
style by using the\tikzstyle{}
command, - use math equation environments including
\usepackage{amsmath, amsfonts, amssymb}
.
- define the
body
codes:- use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - use
\node
to define nodes and text boxes in the Bayesian network, - use
\path
to define arrows in the Bayesian network, - use
\plate
to define plates in the Bayesian network.
- use
Please click on the image and check out the source code. Instead of multivariate vector autoregressive process on temporal factors, we can also use univariate autoregressive process to rebuild BTMF. The Beyasian network is available at btmf_net.png, you can also check out the source code btmf_net.tex.
These two examples show the partially observed time series matrix and tensor, respectively. To draw both two examples, we can follow these steps:
-
preamble
codes:- define the
documentclass
asstandalone
, - use the package
tikz
.
- define the
-
body
codes:- use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - use
\node
to define nodes and text boxes in the graphical model, - use
\path
to define arrows in the graphical model.
- use
These two examples show the time series prediction in the presence of missing values. To draw both two examples, we can follow these steps:
-
preamble
codes:- define the
documentclass
asstandalone
, - use the package
tikz
.
- define the
-
body
codes:- use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - use
\node
to define nodes and text boxes in the graphical model, - use
\path
to define arrows in the graphical model.
- use
These two examples show the undirected and circulant graphs on the relational data samples with certain degrees. To draw both two examples, we can follow these steps:
-
preamble
codes:- define the
documentclass
asstandalone
, - use the package
tikz
.
- define the
-
body
codes:- use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - use
\node
to define nodes, - use
\path
to define arrows.
- use
This example is from the following paper:
Xinyu Chen, Zhanhong Cheng, Nicolas Saunier, Lijun Sun (2022). Laplacian convolutional representation for traffic time series imputation. arXiv preprint arXiv: 2212.01529.
which gives a graphical illustration of the Laplacian convolutional model for time series imputation. To draw the example, we can follow these steps:
preamble
codes:- define the
documentclass
asstandalone
, - use the package
tikz
.
- define the
body
codes:- use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - use
\begin{axis} \end{axis}
and\addplot
to draw coordinates, - use
\node
and\path
to draw nodes and arrows.
- use
These two examples show the probability density function (PDF) and cumulative density function (CDF) of Erlang distribution, respectively. To draw the example, there are few steps to follow:
preamble
codes:- define the
documentclass
asstandalone
, - use the package
pgfplots
, - set font style and adjust
\pgfplotsset{}
as you prefer.
- define the
body
codes:- use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - use
\begin{axis} \end{axis}
to draw the function, - use
\addplot
to define the function for drawing.
- use
![drawing](https://raw.githubusercontent.com/xinychen/awesome-latex-drawing/master/pgfplots-function/normal_pdf.png)
This example shows the PDF of normal distribution with different means and variances. To draw this example, there are few step to follow:
preamble
codes:- define the
documentclass
asstandalone
, - use the package
pgfplots
, - set font style and adjust
\pgfplotsset{}
as you prefer.
- define the
body
codes:- use
\pgfmathdeclarefunction
to define the PDF function of normal (Gaussian) distribution, - use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - use
\begin{axis} \end{axis}
to draw the function, - use
\addplot
to define the function for drawing.
- use
- More posts:
These two examples show the PDF and joint PDF of Lognormal distributions, respectively. There are some recommended material to follow the examples:
- More posts:
These two examples show the iterative process of conjugate gradient for solving a system of linear equations.
These two examples show the random and non-random missing patterns. To draw both two examples, we can follow these steps:
preamble
codes:- define the
documentclass
asstandalone
, - use both
tikz
andpgfplots
packages, - use
\begin{filecontents} \end{filecontents}
to contain the data.
- define the
body
codes:- use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - use
\begin{axis} \end{axis}
to draw the function, - use
\addplot
to draw the data.
- use
This example shows four sequences consisting of Sine and Cosine functions. When regarding these sequences as time series, then we can see two kinds of temporal dynamics, that is, one is from Sine function, while another is from Cosine function. To draw this example, we can follow these steps:
preamble
codes:- define the
documentclass
asstandalone
, - use the package
pgfplots
, - set font style and adjust
\pgfplotsset{}
as you prefer.
- define the
body
codes:- use
\pgfmathdeclarefunction
to define the PDF function of normal (Gaussian) distribution, - use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - use
\begin{axis} \end{axis}
to draw the function, - use
\addplot
to define the function for drawing.
- use
This example illustrates the mechanism of time series forecasting on streaming data in our tracebase project. To draw this example, we can follow these steps:
![drawing](https://raw.githubusercontent.com/xinychen/awesome-latex-drawing/master/pgfplots-function/forecasting.png)
preamble
codes:- define the
documentclass
asstandalone
, - use both
tikz
andpgfplots
packages, - set font style and adjust
\pgfplotsset{}
as you prefer.
- define the
body
codes:- use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - use
\begin{axis} \end{axis}
to define the graphic, - use
addplot
to define the marks at some coordinates and specify the color of these marks.
- use
![drawing](https://github.com/xinychen/tracebase/raw/main/graphics/prob.png?raw=true)
This example gives a graphical illustration of the multivariate time series forecasting problem with missing values. To draw this example, we can follow these steps:
preamble
codes:- define the
documentclass
asstandalone
, - use the package
tikz
.
- define the
body
codes:- set
\Depth
,\Width
, and\Height
parameters by using\newcommand
, - use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - use
\draw
,\filldraw
, and\node
commands to define rectangles and nodes.
- set
This example gives a graphical illustration of the temporal matrix factorization. To draw this example, we can follow these steps:
preamble
codes:- define the
documentclass
asstandalone
, - use the package
tikz
.
- define the
body
codes:- use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - use
\draw
and\filldraw
to set nodes and rectangles.
- use
![drawing](https://github.com/xinychen/awesome-latex-drawing/raw/master/Framework/rolling_prediction.png?raw=true)
This example gives a graphical illustration of the rolling time series forecasting with temporal matrix factorization. To draw this example, we can follow these steps:
preamble
codes:- define the
documentclass
asstandalone
, - use the package
tikz
.
- define the
body
codes:- use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - use
\draw
and\filldraw
to set nodes and rectangles.
- use
This example gives a graphical illustration of a third-order tensor. To draw this example, we can follow these steps:
![drawing](https://raw.githubusercontent.com/xinychen/awesome-latex-drawing/master/TensorFactorization/tensor.png)
preamble
codes:- define the
documentclass
asstandalone
, - use both
tikz
andtikz-3dplot
packages.
- define the
body
codes:- set
\Depth
,\Width
, and\Height
parameters by using\newcommand
, - use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - define the coordinates of the tensor edges,
- use
\draw
command to define nodes.
- set
This example gives a graphical illustration of classical CP tensor factorization on a third-order tensor. To draw this example, we can follow these steps:
![drawing](https://raw.githubusercontent.com/xinychen/awesome-latex-drawing/master/TensorFactorization/CP_factorization.png)
preamble
codes:- define the
documentclass
asstandalone
, - use both
tikz
andtikz-3dplot
packages.
- define the
body
codes:- set
\Depth
,\Width
, and\Height
parameters by using\newcommand
, - use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - define the coordinates of the tensor edges,
- use
\draw
command to define nodes.
- set
This example gives a graphical illustration of augmented tensor factorization model. To draw this example, we can follow these steps:
preamble
codes:- define the
documentclass
asstandalone
, - use both
tikz
andtikz-3dplot
packages.
- define the
body
codes:- set
\Depth
,\Width
, and\Height
parameters by using\newcommand
, - use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - define the coordinates of the tensor edges,
- use
\draw
command to define nodes.
- set
This example gives a graphical illustration of tensor completion task and its framework including data organization and tensor completion, in which traffic measurements are partially observed. To draw this example, we can follow these steps:
- Request:
- upload curve1.pdf,
- upload curve2.pdf.
preamble
codes:- define the
documentclass
asstandalone
, - use both
tikz
andtikz-3dplot
packages.
- define the
body
codes:- set
\Depth
,\Width
, and\Height
parameters by using\newcommand
, - use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - define the coordinates of the tensor edges,
- use
\draw
command to define nodes.
- set
This example gives a graphical illustration of low-rank autoregressive tensor completion model. To draw this example, we can follow these steps:
preamble
codes:- define the
documentclass
asstandalone
, - use both
tikz
andtikz-3dplot
packages.
- define the
body
codes:- set
\Depth
,\Width
, and\Height
parameters by using\newcommand
, - use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - use
\node
,\path
,\plate
commands to define nodes, arrows, and plates, - define the coordinates of the tensor edges,
- use
\draw
command to define nodes.
- set
This example gives a graphical illustration of singular value thresholding process of the tensor data with unitary transform. To draw this example, we can follow these steps:
preamble
codes:- define the
documentclass
asstandalone
, - use both
tikz
andtikz-3dplot
packages.
- define the
body
codes:- set
\Depth
,\Width
, and\Height
parameters by using\newcommand
, - use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - use
\node
command to define nodes, - define the coordinates of the tensor edges,
- use
\draw
command to define nodes, - use
\filldraw
command to define colors of rectangles.
- set
This example gives a graphical illustration of low-rank tensor completion model. To draw this example, we can follow these steps:
- Request:
- upload input_tensor.pdf,
- upload output_tensor.pdf.
preamble
codes:- define the
documentclass
asstandalone
, - use both
tikz
andtikz-3dplot
packages, - use the
algorithm2e
package, - use
\usetikzlibrary{positioning, matrix, fit, calc}
.
- define the
body
codes:- use
\begin{algorithm} \end{algorithm}
environment to define algorithm, - use
\matrix
command to position the components, - use
\begin{scope} \end{scope}
to contain the layers.
- use
This example gives a graphical illustration of low-rank tensor regression model. To draw this example, we can follow these steps:
preamble
codes:- define the
documentclass
asstandalone
, - use both
tikz
andtikz-3dplot
packages.
- define the
body
codes:- set
\Depth
,\Width
, and\Height
parameters by using\newcommand
, - use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - use
\draw
command to define nodes.
- set
This example shows the imputation accuracy of some matrix and tensor models. To draw this example, we can follow these steps:
preamble
codes:- define the
documentclass
asstandalone
, - use the package
tikz
.
- define the
body
codes:- use
\begin{tikzpicture} \end{tikzpicture}
to start drawing, - use
\draw
command to define nodes, - use
\pgfuseimage
command to import images.
- use
Most of these examples are from our papers:
-
Xinyu Chen, Zhanhong Cheng, HanQin Cai, Nicolas Saunier, Lijun Sun (2024). Laplacian convolutional representation for traffic time series imputation. IEEE Transactions on Knowledge and Data Engineering. Early access. [Preprint] [Slides] [Data & Python code]
-
Xinyu Chen, Lijun Sun (2022). Bayesian temporal factorization for multidimensional time series prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44 (9): 4659-4673. [Preprint] [DOI] [Slides] [Data & Python code]
-
Xinyu Chen, Mengying Lei, Nicolas Saunier, Lijun Sun (2022). Low-rank autoregressive tensor completion for spatiotemporal traffic data imputation. IEEE Transactions on Intelligent Transportation Systems, 23 (8): 12301-12310. [Preprint] [DOI] [Data & Python code] (Also accepted in part to MiLeTS Workshop of KDD 2021, see workshop paper)
-
Xinyu Chen, Yixian Chen, Nicolas Saunier, Lijun Sun (2021). Scalable low-rank tensor learning for spatiotemporal traffic data imputation. Transportation Research Part C: Emerging Technologies, 129: 103226. [Preprint] [DOI] [Data] [Python code]
-
Xinyu Chen, Lijun Sun (2020). Low-rank autoregressive tensor completion for multivariate time series forecasting. arXiv preprint arXiv: 2006.10436. [Preprint] [Data & Python code]
-
Xinyu Chen, Jinming Yang, Lijun Sun (2020). A nonconvex low-rank tensor completion model for spatiotemporal traffic data imputation. Transportation Research Part C: Emerging Technologies, 117: 102673. [Preprint] [DOI] [Data & Python code]
-
Xinyu Chen, Zhaocheng He, Yixian Chen, Yuhuan Lu, Jiawei Wang (2019). Missing traffic data imputation and pattern discovery with a Bayesian augmented tensor factorization model. Transportation Research Part C: Emerging Technologies, 104: 66-77. [DOI] [Slides] [Data] [Matlab code] [Python code]
-
Xinyu Chen, Zhaocheng He, Lijun Sun (2019). A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation. Transportation Research Part C: Emerging Technologies, 98: 73-84. [Preprint] [DOI] [Data] [Matlab code] [Python code]