
    Determine if a given social network's interaction matrix represents a perfectly balanced community by checking if the sum of interaction scores in each row, column, and diagonal are equal. The interaction matrix contains numerical scores representing the engagement strength between users, where each user appears exactly once in both rows and columns. A balanced community would indicate optimal distribution of social connections and engagement across all members.
    

    
def magic_square_test(my_matrix):
    """
    Determine if a given social network's interaction matrix represents a perfectly balanced community by checking if the sum of interaction scores in each row, column, and diagonal are equal. The interaction matrix contains numerical scores representing the engagement strength between users, where each user appears exactly once in both rows and columns. A balanced community would indicate optimal distribution of social connections and engagement across all members.
    

    """
def magic_square_test(my_matrix):
    """
    Determine if a given social network's interaction matrix represents a perfectly balanced community by checking if the sum of interaction scores in each row, column, and diagonal are equal. The interaction matrix contains numerical scores representing the engagement strength between users, where each user appears exactly once in both rows and columns. A balanced community would indicate optimal distribution of social connections and engagement across all members.
    

    """
    iSize = len(my_matrix[0])
    sum_list = []
    sum_list.extend([sum(lines) for lines in my_matrix])
    for col in range(iSize):
        sum_list.append(sum((row[col] for row in my_matrix)))
    result1 = 0
    for i in range(0, iSize):
        result1 += my_matrix[i][i]
    sum_list.append(result1)
    result2 = 0
    for i in range(iSize - 1, -1, -1):
        result2 += my_matrix[i][i]
    sum_list.append(result2)
    if len(set(sum_list)) > 1:
        return False
    return True