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Sign in to get your referral linkFrom network graphs and community detection to centrality metrics and information diffusion cascades - create publication-ready social network figures with AI-generated Python code.
Social network data spans relational graphs, temporal interaction logs, influence metrics, and community membership. Each requires specialized layout algorithms and visual encodings to reveal hidden structure in connected data.
Force-directed and spring layouts revealing cluster structure and bridge nodes
Louvain, modularity-based, and label propagation clustering with colored partitions
Log-log histograms with power-law fitting to identify scale-free properties
Betweenness, closeness, and eigenvector centrality mapped across the network
Sorted and clustered matrix views exposing block-diagonal community patterns
Temporal spread of information, influence, or contagion through network layers
Detect communities, measure clustering coefficients, and compute shortest path lengths - all from a natural language prompt.
Centrality, betweenness, PageRank, and eigenvector scores visualized as node-level heatmaps and ranked bar charts.
Export to SVG/PDF sized for Social Networks, Network Science, and PNAS column widths with correct DPI and fonts.
Zoom into subgraphs, filter by centrality thresholds, and highlight ego networks with interactive Plotly outputs.
A small-world network of 60 nodes partitioned into four communities. Node size encodes degree centrality and hub nodes are highlighted with rings. Edit the code to adjust edge probabilities or add new communities.
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Source-target edge list with weights for graph visualization
User interaction features for churn prediction networks
Multi-dimensional data for community detection algorithms
Experimental outcomes for network intervention analysis
Labeled text corpus for sentiment-based network grouping
Log-log degree histogram of a scale-free network with fitted power law exponent. The complementary CDF panel confirms the heavy-tailed distribution characteristic of real social networks.
Preparing preview
Running once automatically on first load
This is a safe playground for learning! Try changing:
Edit the code, run it, then open the full data visualization tool to continue with your own dataset.
Chart gallery
Interactive examples with ready-to-run code
Displays values for two variables as points on a Cartesian coordinate system.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
import pandas as pd
# Generate sample data
np.random.seed(42)
n_samples = 200
height = np.random.normal(170, 8, n_samples)
weight = height * 0.6 + np.random.normal(0, 8, n_samples) - 50
Represents data values as colors in a two-dimensional matrix format.
Sample code / prompt
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
# Create correlation matrix for financial metrics
metrics = ['Revenue', 'Profit', 'Expenses', 'ROI', 'Customers', 'AOV', 'Marketing', 'Employees']
correlation_data = np.array([
[1.00, 0.85, -0.45, 0.72, 0.88, 0.65, 0.72, 0.55],
[0.85, 1.00, -0.78, 0.92, 0.75, 0.58, 0.63, 0.48],
Compares categorical data using rectangular bars with heights proportional to values.
Sample code / prompt
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
# Generate performance scores for 5 treatment groups
np.random.seed(42)
groups = ['Control', 'Treatment A', 'Treatment B', 'Treatment C', 'Treatment D']
n_samples = 30
Displays the distribution of numerical data by grouping values into bins.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde, skewnorm
# Generate age data with slight right skew
np.random.seed(42)
ages = skewnorm.rvs(a=2, loc=42, scale=15, size=500)
ages = np.clip(ages, 18, 80) # Clip to realistic range
fig, ax = plt.subplots(figsize=(12, 7))
Displays data points connected by straight line segments to show trends over time.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
# Generate temperature data for 3 major US cities over 12 months
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
nyc = [30, 32, 40, 52, 65, 75, 82, 81, 74, 63, 50, 38]
miami = [65, 66, 70, 76, 82, 87, 90, 90, 87, 80, 72, 66]
chicago = [25, 27, 35, 48, 62, 72, 80, 79, 71, 60, 45, 32]
# Create figure with enhanced stylingUpload your edge list or adjacency data, describe the plot you need, and get publication-ready network figures with full Python code in seconds.
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