| Name | SMAdiffz JSON |
| Version |
1.1.1
JSON |
| download |
| home_page | |
| Summary | Powerful data structures for data analysis, time series for information diffusion analysis |
| upload_time | 2023-11-12 04:53:56 |
| maintainer | |
| docs_url | None |
| author | H.M.M.Caldera |
| requires_python | |
| license | |
| keywords |
python
social media
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
The proposed package is designed to facilitate comprehensive work on information diffusion analysis.
It provides a versatile set of tools and functionalities that empower users to explore, model, and analyze the intricate dynamics
of information spread within a given system.
Following is a sample scenario.
def measure_information_diffusion(posts, threshold):
# Sort the set of posts . This should using timestamps
posts = sorted(posts, key=lambda x: x.timestamp)
# Initialize empty set of trees
trees = set()
# Iterate over each post in the sorted set
for i, p_i in enumerate(posts):
# Initialize a new tree with a single node representing (p_i)
T_i = {p_i}
# For each post with a timestamp later than p_i
for j in range(i + 1, len(posts)):
p_j = posts[j]
# Compute the similarity between the tags of p_i and p_j
similarity = compute_similarity(p_i, p_j)
# If similarity is above the threshold, add a directed edge from p_i to p_j in T_i
if similarity > threshold:
T_i.add(p_j)
# If p_j has already been added to a diffusion tree in trees, merge T_i with that tree
for T_j in trees:
if p_j in T_j:
T_j.update(T_i)
break
else:
# If p_j hasn't been added to any diffusion tree, add T_i to trees
trees.add(T_i)
# Return the set of diffusion trees
return trees
Raw data
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